This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

We introduce quantum spatio-temporal dynamics (QSD) as modeled by the Nexus Paradigm (NP) of quantum gravity to resolve the problem of energy- momentum localization in a gravitational field. Currently, the gravitational field as described using the language of geometry modeled under General Relativity (GR) fails to provide a generally accepted definition of energy-momentum. Attempts at resolving this problem using geometric methods have resulted in various energy-momentum complexes whose physical meaning remain dubious since the resulting complexes are non-tensorial under a general coordinate transformation. In QSD, the tangential manifold is the affine connection field in which energy-momentum localization is readily defined. We also discover that the positive mass condition is a natural consequence of quantization and that dark energy is a Higgs like field with negative energy density everywhere. Finally, energy-momentum localization in quantum gravity shows that a free falling object will experience large vacuum fluctuations (uncertainties in location) in strong gravity than in weak gravity and that the amplitudes of these oscillations defines the energy of the free falling object.

Model-based approaches form the basis of the majority of damage detection and localization studies; however, built-in online detection technique can offer an alternative for future structural health monitoring technologies. Thus, in this paper, we present a dynamic localization algorithm for damage localization in functionally graded plates. The method depends on the creation of a grid matrix that contains the dynamic response of the structure over time. Subsequently, an optimization process is carried out via a linear equation representing the information contained within the grid to achieve accurate localization of damage. Due to the inherent sparsity of the localization nature, the FISTA algorithm has been employed as a problem solver. The approach is tested in the case of the functionally graded plate under clamped free boundary conditions. Several, damage scenarios were investigated for the case of damage signals on and off the grid. The results show that the proposed approach is capable of accurately predicting damage position and can be suitable for use in low size data systems.

Radio techniques are widely used in wildlife tracking. Currently, the most common tracking methods include radio tracking (operating on VHF and UHF frequencies), satellite tracking (e.g., Argos satellite Doppler-based positioning system), and Global Navigation Satellite System (GNSS) tracking. One of the radio tracking systems is the Radio-Frequency Identification (RFID) system, characterized by small dimensions and a long operational period due to energy-efficient features. The advantages of RFID make it possible to apply this technique to track oak acorns in the process of zoochoric dispersal. In our study, we explored the potential applications of RFID systems for monitoring the dispersal of oak acorns. We developed a tracking system based on a semi-passive RFID tag, which we tested under laboratory and quasi-realistic conditions. The obtained results confirm the feasibility of using our system in radio tracking small objects such as oak acorns.

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

The sensor nodes have limited computation, sensing, communication capabilities and generally operated by batteries in a harsh atmosphere with non-replenish able power sources. These limitations force the sensor network subject to failure because most of the energy is spent on sensing, computing and data transmission. This paper introduces an Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) to assist Wireless Sensor Networks (WSNs) by (a) extending the lifespan of the network (b) effectively using the battery power (c) decreasing the network overhead and (d) ensuring a high packet transmission ratio with minimal delay. The delay time-based Cluster Head (CH) is elected based on the node degree, residual energy and Received Signal Strength (RSS) to accomplish the goal. Additionally, the RSS-based network partitioning is implemented to evaluate the gradient based on demand routing between source (sensing node) and destination (BS). Whenever the current CH residual energy goes under the threshold level, the proposed protocol performs the clustering process, reducing the exchange of control packets. However, the BS periodically gathers the data from every single CH which helps to reduce the collision and Medium Access Control (MAC) layer conflict. From the simulation results, it is the evident that the proposed protocol performance in terms of average end-to-end latency, packet delivery ratio, average energy consumption and control overhead is better than the well-known current protocols.

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. This study presents arguments for a framework that approaches the geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characters of the geothermal exploitation beyond its geological conditions. It aims to conceptualise a community based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

Ocean wave energy is a significantly well-liked renewable energy source. The Internet of Things (IoT) offers a great opportunity for development of the ocean wave energy research. The IoT technology significantly enhanced the performance of the ocean wave energy converter (WEC). The data obtained from IoT devices reduce the risk of using WEC. However, Integrating IoT devices with the WEC is a daunting task because controlling the WEC needs real-time data. This paper investigated the performance of WEC and estimate the lifetime of the modules. IoT devices integrated with various sensors determine different parameters and conditions of the ocean. The IoT platform is used for wireless voltmeters, wireless AC current meters, wireless voltage detection, wireless resistance sensors, wireless temperature sensor, wireless accelerometer, wireless pressure meters, and wireless water level sensors to determine the real-time condition of the ocean wave. These devices are also proven to be reliable and accurate.

Vehicle driveability is one of the important vehicle attributes in range-extender electric vehicles due to the electric motor torque characteristics at low-speed events. The process of validating and rectifying vehicle driveability attributes is typically utilised by a physical vehicle prototype that can be expensive and required several design iterations. In this paper, a model-based energy method to assess vehicle driveability is presented based on a high-fidelity 49 degree-of-freedom powertrain and vehicle systems. Multibody dynamics components were built according to their true centre of gravity relative to the vehicle datum for providing an accurate system interaction. The work covered a frequency at less than 20 Hz. The results that consisted of the component frequency domination are structured and examined to identify the low-frequency sensitivity based on different operating parameters such as a road surface coefficient. An energy path technique was also implemented on the dominant component by decoupling its compliances to study the effect on the vehicle driveability and low-frequency response. The outcomes of the research provided a good understanding of the interaction across the sub-systems levels. The powertrain rubber mounts were the dominant components that controlled the low-frequency contents (< 15.33 Hz) and can change the vehicle driveability quality.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

This paper aims to propose a new approach of territorial competitiveness assessment revisited from the resource-based view, as the combination of location-specific resources and capabilities can improve the territorial socio-economic development. A territorial competitiveness index is calculated in order to assess the potential of renewable energy sources to improve the sustainable development in islands. Different sources of information and methodologies have been employed to measure the variables included in the model, thus ensuring a rigorous process in the index calculation. In order to quantify the basic resources, for example, a methodology based on a multicriteria analysis (MCA) with geographic information system (GIS) is suggested, with the objective of obtaining an indicator called index of available territorial resources. This index synthesizes the map information through a numerical value that allows integrating the territorial resource with other indicators of the model. The results of the study show that capability development is a key factor to better exploit the territorial resource endowment in order to achieve a competitive advantage.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

There is an international concern for reducing energy consumption and protecting the environment in the construction industry. The energy efficiency of a building depends on the thermal resistance of concrete, and utilizing concrete products with elevated thermal resistance can enhance this aspect. This study analyzes the impact of perlite, as an addition, on the physico-mechanical properties of concrete, with the aim of identifying perlite concrete products that will respond to the improvement of the heat transfer resistance of the building envelope. The study examines the effect of replacing different amounts of concrete aggregates with perlite in percentages of 10%, 20%, 30%, and 100%. The results show that substituting sand with perlite improves the thermal performance of concrete, but its mechanical properties are negatively affected. The research carried out highlighted that perlite can be successfully introduced in the production of concrete blocks used for external non-bearing wall panels, the outstanding feature being the reduction of energy consumption in buildings. Overall, the paper identifies an efficient and environmentally friendly way of using perlite in the construction industry, enhancing the energy efficiency of buildings while minimizing the environmental footprint caused by the construction industry.

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.

To compare the quality of low-energy VMIs obtained with three DECT platforms according to phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs (SFCT-1st and SFCT-2nd) and on one Dual-source CT (DSCT). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’) were assessed on VMIs from 40 to 70keV. Highest noise magnitude values were found with SFCT-1st and noise magnitude was higher with DSCT than with SFCT-2nd for 26-cm (10.2±1.3%) and 31-cm (7.0±2.5%), and the opposite for 36-cm (-4.2±2.5%). The highest average NPS spatial frequencies and TTF values at 50% (f50) values were found with DSCT. For all energy levels, f50 values were higher with SFCT-2nd than SFCT-1st for 26-cm (3.2±0.4%) and the opposite for 31-cm (-6.9±0.5%) and 36-cm (-5.6±0.7%). The lowest d’ values were found with SFCT-1st. For all energy levels, d’ values were lower with SFCT-2nd than with DSCT for 26-cm (-6.2±0.7%), similar for 31-cm (-0.3±1.9%) and higher for 36-cm (5.4±2.7%). In conclusion, compared to SFCT-1st, SFCT-2nd exhibited lower noise magnitude and higher detectability values. Compared with DSCT, SFCT-2nd had lower noise magnitude and higher detectability for the 26-cm but the opposite was true for the 36-cm.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

Thermostatically controlled loads have great potential to make a significant contribution to improving energy efficiency in the building sector, which is responsible for 40% of greenhouse gas emissions in the EU. This, in addition to the environmental damage, represents a huge expense in the electricity bill. Therefore, it is very important to train engineers on how to design energy management systems for TCLs. With this goal in mind, it would be very useful to have a simulation-based educational tool (SBET) to understand thermostatically controlled loads, their characteristics and possibilities in energy efficiency. In addition, it would be very useful if this tool could be introduced in engineering curricula to help students to be better trained and enter the labor market with more opportunities. Based on the shortcomings detected, this work develops a SBET specifically designed to teach about TCLs (SBET-TCLs), both about their intrinsic characteristics and their better management. To verify the developed SBET-TCL, it has been tested in a real scenario: A survey was carried out among the students of the subject 'Alternative Energy Sources' of the degrees of Industrial Engineering. The results show that the use of SBET-TCL has very positive effects on the learning process.

Infectious disease like COVID-19 will have a great impact on human society. An effective prevention and control measure is to quickly isolate the source and reduce the population’s exposure to the infection. Therefore, it is very important to quickly trace and identify the virus carrier and its close contacts in order to effectively suppressing transmission. This article provides a method, system and storage medium for locating the source of infection based on big data. We adopt the method of combining Bluetooth and GPS positioning to track the action trajectory of each individual. The method stores the close contact and location information locally and update on the cloud platform for an appropriate period of time. The method and system can provide personal risk levels and regional risk levels through related algorithms. When entering a high-risk area or if the personal risk level within the Bluetooth interconnection range is greater than the set threshold, an alarm function is provided. We can also generate reports on the spread of the epidemic over a period of time. As a result, the system can quickly locate the source of infection and close contacts, providing a reference for quick and highly efficient epidemic prevention and control.

It is desirable to upgrade previous evolutionary theories, which have remained incomplete and controversial for decades. Here we employ the concept of carbon-based entities (CBEs), which include methane, amino acids, proteins, organisms, and other entities containing relatively many carbon atoms. We deduce the driving force, mechanisms, steps, modes, tempos of CBE evolution, through integration of biology, physics, and chemistry using logics for complex issues. We hence establish the Carbon-Based Evolutionary Theory (CBET). The CBET suggests that evolution is the increase in hierarchy, diversity, fitness of CBEs under natural selection and driven by thermodynamics due to the chemical effect of the thermodynamic features of the Earth on CBEs. It provides better explanations for life origin, macroevolution events, natural selection, sympatric speciation, and evolution tempos than previous evolutionary theories. It reveals the evolutionary basis of multiple important social notions, including diversity, collaboration, altruism, obeying rules, and proper increase in freedom. It refutes some wrong notions in thermodynamics, including negative entropy (negentropy) and that biological order is equal to thermodynamic order, which have misled many people. The CBET is supported by its deduction and application. It could be a rare bridge linking laws of thermodynamics, evolution of life, and development of human society, and could have great significance in various sciences.

Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

Student attendance serves many other important purposes aside from monitoring. In certain universities, the attendance of students in a course is also used as one of the requirements for students to be allowed to sit for the final examination. Traditionally, among most Malaysian Institutions of Higher Learning (IHL), attendance recording is usually done using pen and paper, or uses simple web-based system that is time consuming and difficult for faculty periodic monitoring. To address the identified drawbacks, this research aims to develop a Smart Attendance for Faculty Monitoring System using the Bluetooth Low Energy (BLE) technology to assist faculty in recording, managing and monitoring students’ attendance and class schedules effectively. The system is developed for Android-based devices using an agile methodology consists of iteration and incremental approaches. Thus, to evaluate the effectiveness of the system, a survey was conducted on 140 respondents involving lecturers and students of Kolej Universiti Poly-Tech MARA (KUPTM). Respondents were selected using purposive sampling. The descriptive analysis showed that 87.9% of the respondents strongly agreed that the system is effective in assisting lecturers to record attendance, manage class schedules and student attendance as well as to assist faculty in monitoring students’ absenteeism.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

Metal hydrides are one of the types of functional materials that allow safe and compact storage of a large amount of hydrogen, which is increasingly used today as an alternate fuel or energy source. The possibility of obtaining the initial energy necessary for the production of hydrogen by electrolysis process from renewable energy sources, such as solar panels and wind generators, makes hydrogen energetic quite attractive and rapidly developing industry sector. Solid form of hydrogen storage with the possibility of reversible sorption, gives opportunity for creation autonomous energy storage systems. La-Ni based alloys allow hydrogen storing at ambient temperatures and pressure not higher than 15 bar, which makes the application of these alloys quite practical, interesting and prospects for analysis and modifications on the ways of stored hydrogen capacity increasing, alloys price reducing and application for renewable energy storage.

Various renewable energy sources such as wind power and photovoltaic (PV) have been increasingly integrated into the power system through power electronic converters in recent years. However, power electronic converter-driven stability issues under specific circumstances, for instance, modal resonances might deteriorate the dynamic performance of the power systems or even threaten the overall stability. In this paper, the integration impact of a hybrid renewable energy source (HRES) system on modal interaction and converter-driven stability is investigated in an IEEE 16-machine 68-bus power system. Firstly, an HRES system is introduced, which consists of full converter-based wind power generation (FCWG) and full converter-based photovoltaic generation (FCPV). The equivalent dynamic models of FCWG and FCPV are then established, followed by the linearized state-space modeling. On this basis, converter-driven stability analyses are performed to reveal the modal resonance mechanisms of the interconnected power systems and the modal interaction phenomenon. Additionally, time-domain simulations are conducted to verify effectiveness of dynamic models and support the converter-driven stability analysis results. To avoid detrimental modal resonances, an optimization strategy is further proposed by retuning the controller parameters of the HRES system. The overall results demonstrate the modal interaction effect between external AC power system and the HRES system and its various impacts on converter-driven stability.

Carbon supported nanoparticles of monometallic Ni catalyst and binary Ni-Transition Metal (Ni-TM/C) electrocatalytic composites were synthesized via chemical reduction method, where TM stands for the doping elements Fe, Co, and Cu. The chemical composition, structure and morphology of the Ni-TM/C materials were characterized by X-ray photoelectron spectroscopy (XPS), X-ray diffractometry (XRD), transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDS). The electrochemical properties towards hydrogen oxidation reaction in alkaline medium were studied using the rotating disc electrode and cycling voltammetry methods. A significant role of the TM dopant in the promotion of the hydrogen electrooxidation kinetics of the binary Ni-TM/C materials were revealed. A record-high in exchange current density value of 0.060 mA cm²_Ni was measured for Ni₃Fe₁/C, whereas the monometallic Ni/C counterpart has only shown 0.039 mA cm²_Ni. In order to predict the feasibility of the electrocatalysts for hydrogen chemisorption, density functional theory was applied to calculate the hydrogen binding energy and hydroxide binding energy values for bare Ni and Ni₃TM₁.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Issues related to safe and abundant energy production have been prominent in recent years. This is particularly tr ue when society considers how to increase the quality of life by providing low-cost energy to citizens. A significant concern of the Gulf Cooperation Council (GCC) relates to the environmental effects of energy production and energy use associated with climate change. Efforts to reduce fossil fuel use and increase the use of renewable energy, together with the price volatility of fossil fuels, have seriously impacted the economics of many of the oil-producing countries, particularly the Gulf States, which has led to efforts to make their economies more diverse and less dependent on oil production.

Background: This study examines the relationship between energy usage, health issues, and pro-environmental behavior (PEB) in Kyrgyzstan, amidst the country's commitment to transition from coal-based energy to renewable sources in line with the Paris Agreement. The purpose is to investigate citizens' attitudes towards PEB and their intentions to engage in environmentally friendly actions, focusing on gas, electricity, and coal. Methods: Drawing upon the Theory of Planned Behavior (TPB) framework, a survey was conducted among 1,455 respondents to explore attitudes towards PEB and energy sources' impact on health issues. Results: Decarbonization efforts in Kyrgyzstan and Central Asia are in their early stages, with coal remaining a primary energy source. The study emphasizes the importance of governmental policies and citizen action in achieving decarbonization goals. Rising electricity costs outweigh the increase in indirect energy costs for food, posing challenges for households in adapting to changing energy dynamics. Conclusions: Targeted interventions and communication strategies are crucial to promote pro-environmental behavior and facilitate the transition to sustainable energy sources. Understanding the relationships between health concerns, air pollution awareness, PEB, and energy source choices can inform policymakers and organizations in their efforts to ensure a sustainable and healthy future for Kyrgyzstan and other Central Asian countries.

The world is facing an energy crisis. Governments are seeking to provide universal energy access and guarantee energy security while trying to mitigate climate change. One possible solution is energy transitions towards low carbon energy systems. Among other things (physical infrastructure, public policy and regulatory enablers and knowledge and capacities) changes in the energy systems require a well informed and participative citizenship. Within this context the concept of energy literacy appears. Energy literacy is the understanding of how energy is generated, transported, stored, distributed and used, awareness about its environmental and social impacts and the knowledge to use it efficiently in the different sectors of the economy. This paper provides a systematic literature review in the Web of Science’s Core Collection. Most of the work done around energy literacy addresses its evaluation among different groups, particularly students at different levels, and the construction, application and evaluation of tools for improving energy literacy. Other frequently studied issues are the influence of energy literacy in decision making, its drivers and conceptual research about the topic. Energy enables citizens to effectively contribute to energy efficiency and sustainable development, nevertheless energy literacy is not strongly correlated to energy consumption habits.

The vast array of interconnected microbes across earth’s ecosystems and within holobionts has been called the “Internet of Microbes.” Research into the fundamental microbial properties has demonstrated that bacteria and archaea are masters of energy and information collection, storage, transformation, and dissemination using both “wired” and wireless (at a distance) capacities. These features suggest that microbes are quantum entangled within and between earth’s holobionts, and that specific tools affecting energy and information functions could provide an effective strategy for managing microbial populations. Microbes use four critically important microbial networking functions (sound, light, electricity, and magnetism). This narrative review focuses on microbial management based on two of the four: sound and light (as well as related vibrations). These are examined as: 1) tools for managing microbial populations, 2) tools to support new technologies, and 3) tools for healing humans and other holobionts. Given microbial sensitivity to sound, light, and vibrations, it is critical that microbial populations and microbe-laden holobionts are protected from potentially damaging exposures. We conclude that sound, light, and vibrations are useful therapeutic tools that can help support useful microbial populations and help to address ongoing challenges of holobiont disease.

Daylight usage in buildings improves visual comfort and lowers the final energy demand for artificial lighting. The question always occurs: how much conservation can you achieve? New upcoming or rare materials and constructions have a lack of information about their application. Therefore, the current work investigates the daylight performance of a multi-layer textile membrane roof with 2 300 m² on top of a sports hall. A translucent, thermal insulation with a glass fibre fleece between the roof membranes combines daylight usage and heating demand reduction. A sports hall with built year 2017 is selected as the case study building. The optical properties of the roof construction are measured. The (visual) light transmittance amounts to 0.72 % with a clean surface. An accordingly parametrized climate-based annual daylight modeling delivers daylight indicators for different construction scenarios. The results show that in comparison to only one glass facade, the additional translucent and thermally insulated membrane construction increases the annual daylight autonomy700/ continuous DA700 from 0/ 15 % to 1.5/ 38 %. In the roof covered areas of the sport field, this results in a reduction from 19.7 to 13.8 kWhel/m²/a electricity for the artificial lighting with dim control (30 % savings). Also, the influence of soiling on the light transmittance was determined with a relevant reduction of one layer about a factor 0.81. The novel results are of great value as a comparison and benchmark for planners and future buildings of similar type.

The term sustainable energy is very familiar to all of us yet its exact definition or meaning has so far been vague. To date the widely adopted definition of sustainable energy has been inspired by the definition of sustainable development formulated more than 30 years ago in the Our Common Future - the UN’s Report of the World Commission on Environment and Development. The current definition conceals the true spirit of the Report and this has some ramifications. It raises the question as to whether we have put too much emphasis on attaining the future dimension of sustainability in the middle of the situation where the energy survivability is in fact the real issue in many parts of the world. In this paper the role of energy in sustaining livelihoods of low income communities is discussed. Existing definition of sustainable energy that the whole world seems to have embraced is looked at critically and its downsides exposed. The identified pillars of sustainable energy and the associated issues is discussed and the need for an additional pillar, i.e. affordability is discussed and called for. This paper will argue that global definition of sustainable energy seems inappropriate and should be jettisoned and a more realistic definition should be formulated, adopted and embraced at local (i.e. national or regional) level taking into account local attributes and factors. The current push for a transition from the reliance on conventional energy sources to ‘cleaner’ energy sources (generally associated with renewable energy) has further exacerbated the issue of energy affordability that has made the conditions even more dire for so many groups of the world population. In the midst of this situation, those who push for a rushed energy transition seem to be those who also produce unsustainably high carbon footprint but who can easily offset their carbon foot-print through the ‘net-zero’ concept.

Earth’s climate cannot be ignored any longer. Policies are vital in order to mitigate the negative effects of climate change. The energy crisis created by the Russo-Ukrainian war and pandemic affected the EU and its member states. The focus is more than ever on its energy policies and independence. The EU revised the energy strategy in response to Russia’s blackmail, and it sped up all the processes for energetic independence from Russia. This benefited the climate change policies the most, as all the measures involved reducing energy consumption and increasing renewables, thus contributing to reducing greenhouse gas emissions. As a member state of the EU, Romania is committed to complying with EU regulations. With a high degree of energy independence compared with the other EU members, Romania plans to become a regional energy provider and modernize the energy infrastructure internally as a response to Russia’s unprovoked aggression. The measures that the EU and Romania implemented after the war started in 2022 have come to fruition, and the effects are becoming visible a year later. This study aims to study the energy strategy of Romania in correlation with the EU strategy in the turbulent period of pandemics and war between 2019 and 2023, with the latest available data.

The Universe at last scattering is locally treated as an unbound gas. The internal kinetic energy of the gas effectively constitutes a scalar energy field. The gas’s adiabatic expansion is entropic, giving repulsive entropic pressure. Gas kinetic energy is converted into entropic energy gain (63%) and isoentropic work against gravity (37%) at a constant 63:37 ratio. A three-term expression of the gas’s Hubble parameter is derived and found to be exclusively dependent on its mass density. At last scattering, this model gives a Hubble constant that is 125% of the value found from the ΛCDM model. After partition of Universal mass into the cosmic web of galaxies and the intergalactic medium (IGM), expansion came mostly from the IGM, presently comprising about 84% of total Universal mass and 90% of its volume. The onset of star formation within the cosmic web increased the IGM’s kinetic energy through the action of starlight, giving free electrons as an additional repository. Many of these free electrons are suprathermal. Suprathermal energy from both electrons and protons comprises about half of the IGM’s total kinetic energy and is expressed in the ΛCDM model as “dark energy” Λ. Entropic pressure derives from thermodynamic laws not found within general relativity.

This article aims to assess the benefits of floor-slab insulation measures using extruded polystyrene (XPS) and polyisocyanurate (polyiso) insulation materials at various levels of insulation thicknesses for a detached residential building. The EnergyPlus simulation analysis was carried out within the seven energy zones (represented by eight locations) of South Africa in accordance with the South African national code for building energy efficiency (SANS10400-XA). The energy savings and payback periods due to use of the insulation over a lifecycle period of 50 years were assessed. Cape Town (zone 4) behaved differently from other locations and hardly benefitted from the application of floor-slab insulation measures. Generally, polyiso insulation performed better than XPS, for vertical gap insulation. For vertical gap insulation, lower insulation thicknesses required higher insulation depths to maximize energy savings. Similarly, lower insulation thicknesses required higher perimeter insulation widths to maximize energy savings, for the horizontal perimeter insulation method. The locations that benefitted most from vertical gap floor-slab insulation were Pretoria (zone2), Kimberley (zone6), Nelspruit (zone3), Fraserburg (zone7), Welkom (zone1), Mthatha (zone5), Ixopo (zone5H) and Cape Town (zone4) in that order. This order was almost similar with those for the horizontal perimeter floor-slab insulation and horizontal full floor-slab insulation methods.

Following an updated outlook of global energy production and utilization, we show through selected examples from both developing and developed countries how distributed generation from renewable energy sources, and from solar energy in particular, is the key solution to ending energy poverty across the world. Guidelines aimed at policy makers suggest a systems view of energy that will be instrumental in guiding the transition from fossil fuels to combustion-free renewable energy for all energy end uses.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

Many theoretical models of iron-based superconductors have been proposed but Tc calculations based on the models are usually missing. We have chosen two models of iron-based superconductors in the literature and then compute the Tc values accordingly: Recently two models have been announced which suggest that superconducting electron concentration involved in the pairing mechanism of iron-based superconductors may have been underestimated, and that the antiferromagnetism and the induced xy potential may even have a dramatic amplification effect on electron-phonon coupling. We use bulk FeSe, LiFeAs and NaFeAs data to calculate the Tc based on these models and test if the combined model can predict the superconducting transition temperature (Tc) of the nanostructured FeSe monolayer well. To substantiate the recently announced xy potential in the literature, we create a two-channel model to separately superimpose the dynamics of the electron in the upper and lower tetrahedral plane. The results of our two-channel model support the literature data. While scientists are still searching for a universal DFT functional that can describe the pairing mechanism of all iron-based superconductors, we base on the ARPES data to propose an empirical combination of DFT functional for revising the electron-phonon scattering matrix in the superconducting state, which ensures that all electrons involved in iron-based superconductivity are included in the computation. Our computational model takes into account this amplifying effect of antiferromagnetism and the correction of the electron-phonon scattering matrix together with the abnormal soft out-of-plane lattice vibration of the layered structure, which allows us to calculate theoretical Tc values of LiFeAs, NaFeAs and FeSe as a function of pressure that correspond reasonably well to the experimental values. More importantly, by taking into account the interfacial effect between an FeSe monolayer and its SrTiO3 substrate as an additional gain factor, our calculated Tc value is up to 91 K high, and provides evidence that the strong Tc enhancement recently observed in such monolayers with Tc reaching 100 K may be contributed from the electrons within the ARPES range.

This analysis focuses on identifying the most efficient and cost-effective method of supplying power to a remote site, exploring photovoltaics (PV) and small wind turbines as primary power sources, and evaluating battery banks and hydrogen storage fuel cell systems as potential storage options. The hydrogen storage system converts surplus renewable power into hydrogen through an elec-trolyzer, storing it for later use in a fuel cell when renewable sources produce less power, enabling efficient energy storage during peak production periods. A sensitivity analysis of wind speed and hydrogen subsystem cost was conducted to evaluate the hydrogen storage system's performance. The optimal system graph suggests that the hydrogen subsystem must significantly decrease in cost to rival the battery bank, and in most cases, both the hydrogen system and battery bank were recommended together, offering reliable and efficient power for the remote site. While the battery bank is presently the more feasible option for powering the remote site, continuous monitoring and evaluation of both systems, considering site location, energy needs, and available resources, are essential to determine the most suitable power supply approach as technology advances and costs evolve over time.

The water and wastewater sectors are energy-intensive, and so a growing number of utility companies are seeking to identify opportunities to reduce energy use. Though England’s water sector is of international interest, in particular due to the early experience with privatisation, for the time being very little published data on energy usage exists. We analyse telemetry data from Thames Water Utilities Ltd. (TWUL), which is the largest water and wastewater company in the UK and serves one of the largest mega-cities in the world, London. In our analysis, we (1) break down sectoral energy use into their components, (2) present a statistical method to analyse the long-term trends in use, as well as the seasonality and irregular effects in the data, (3) derive energy-intensity (kWh m³) figures for the system, and (4) compare the energy-intensity of the network against other regions in the world. Our results show that electricity use grew during the period 2009 to 2014 due to capacity expansions to deal with growing water demand and storm water flooding. The energy-intensity of the system is within the range of reported figures for systems in other OECD countries. Plans to improve the efficiency of the system could yield benefits in lower the energy-intensity, but the overall energy saving would be temporary as external pressures from population and climate change are driving up water and energy use.

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that alt-hough preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance and an uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limit-ing factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bac-terial proteins are targeted being the biosynthesis of the cell wall and the energetic metabolism two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and how chemical probes for protein profiling enabled the identification of new targets and bi-omarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Lake Rawapening, Semarang Regency, Indonesia, has incorporated a holistic plan in its management practices. However, despite successful target achievements, some limitations remain that a review of its management plan is needed. This paper identifies and analyzes existing lake management strategies as a standard specifically in Lake Rawapening by exploring various literature, both legal frameworks and scholarly articles indexed in Google Scholar and published in Water by MDPI about lake management in many countries. There are two major types of lake management, namely pillar-based and object-based. While the former is the foundation of a conceptual paradigm that does not comprehensively consider the roles of finance and technology in the lake management, the latter indicates the objects to manage so as to create standards or benchmarks for the implementation of various programs. Overall, Lake Rawapening management should include more programs on erosion-sedimentation control and monitoring of operational performance using information systems.

Nature-based solutions (NbS) are increasingly recognised for their potential to address both the climate and biodiversity crises. These outcomes are interdependent, and both rely on the capacity of NbS to support and enhance the health of an ecosystem: its biodiversity, the condition of its abiotic and biotic elements, and its capacity to function normally despite environmental change. However, while understanding of ecosystem health outcomes of nature-based interventions for climate change mitigation is growing, the outcomes of those implemented for adaptation remain poorly understood with evidence scattered across multiple disciplines. To address this, we conducted a systematic review of the outcomes of 109 nature-based interventions for climate change adaptation using 33 indicators of ecosystem health across eight broad categories (e.g. diversity, biomass, ecosystem functioning and population dynamics). We showed that 88% of interventions with positive outcomes for climate change adaptation also reported measurable benefits for ecosystem health. We also showed that interventions were associated with a 67% average increase in local species richness. All eight studies that reported benefits in terms of both climate change mitigation and adaptation also supported ecosystem health, leading to a triple win. However, there were also trade-offs, mainly for forest management and creation of novel ecosystems such as monoculture plantations of non-native species. Our review highlights two major limitations of research to date. First, only a limited selection of metrics are used to assess ecosystem health and these rarely include key aspects such as functional diversity and habitat connectivity. Second, taxonomic coverage is poor: 67% of outcomes assessed only plants and 57% did not distinguish between native and non-native species. Future research addressing these issues will allow the design and adaptive management of NbS to support healthy and resilient ecosystems, and thereby enhance their effectiveness for meeting both climate and biodiversity targets.

Energy citizenship is increasingly receiving attention and highlights the transformation from passive to active energy consumers who are aware of and responsible for the energy transition. The topic is often associated with energy communities, which are regarded as organizations that allow citizens to directly engage in energy and broader sustainability initiatives. The objective of the article is to examine the extent of public and private engagement in the energy transition among cooperative members as well as the propensity of energy communities to promote empowerment and inclusivity. To this end, we employ a mixed-methods approach, involving a sample of 5402 participants and 20 qualitative interviews in two prominent energy cooperatives in Europe, Ecopower in Flanders and è nostra in Italy. Our results show that although a small group of members demonstrate strong and active engagement, the majority of members prefer to engage informally, while still benefitting from the empowerment provided by their cooperative. Interestingly, one subset of members is more closely aligned with the role of financial investors. Eventually, flexibility in defining and framing expectations of member engagement can play a crucial role in promoting inclusivity within energy communities, especially by bringing closer those relatively less familiar with the issue.

The evolution of transportation has been inextricably tied to the progress of civilization. Through innovation, the automobile business has been working to improve safety, quality, and compliance with environmental regulations. Electric vehicles have made significant strides in this area, but optimizing their efficiency requires a special focus because they expend energy that can be recovered in a variety of ways. Energy harvesting, a cutting-edge technology that captures wasted energy from vehicles, has recently received a lot of attention as it constitutes a means to improve the efficiency of electric vehicles. Dissipated energy can be converted into electricity using regenerative energy recovery systems and put to various uses. This study tenders a thorough examination into energy recovery technologies which could be applied to the various types of energy dissipated in electric vehicles. Firstly, the paper investigates the possible sources of energy recoverable from an electric vehicle, as well as the various types of energy dissipated. Secondly, the article examines the energy recovery technologies most frequently used in vehicles, categorizing them according to the type of energy and application. Finally, it determines that with further research and development, energy harvesting holds considerable potential for improving the energy efficiency of electric vehicles. New and innovative methods for capturing and utilizing wasted energy in electric vehicles can be established. The potential benefits of applying energy recovery systems in electric vehicles is a vital issue for the automobile industry to focus on due to the potential benefits involved. The ongoing progress currently being made in this field is expected to play a significant role in shaping the future of transportation.

Large-scale integration of solar energy technologies in Rome’s built environment epitomizes the needed general adoption of distributed generation via functionalization of buildings of all size and end use across the world, to become active energy generators and no longer energy users only. This paper identifies selected technology solutions and critical policy and educational initiatives to effectively achieve within the next decade (2018-2027) the widespread uptake of decentralized solar energy systems in the built environment on a global scale.

Small modular reactors (SMR) (&lt;300 MW) offer a potentially attractive nuclear energy option for the middle-east region (MER). Currently, the MER uses a significant amount of fossil fuel to process heat applications such as water desalination and in petroleum refineries and chemical plants, besides generating electricity. SMR technologies represent an opportunity to meet future energy demand in the MER. This paper discusses issues related to the future development and use of SMR technology in nuclear-renewable hybrid energy systems for application in the middle east. SMRs have also been examined as part of a resilient hybrid energy system that combines nuclear energy with renewable energy and traditional fossil energy to produce chemicals, fuels, and electricity. This paper presents the results of a techno-economic analysis of a Nuclear-Renewable-Conventional Hybrid Energy System. The paper concludes that SMR technology will be an essential feature of future hybrid energy systems for the MER.

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open source energy system models, it has emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system that is used to reproduce and understand its behaviour under given conditions, without seeking an optimal solution. Given the lack of simulation models that are also fully open source, in this paper a new open source energy system model is presented. The developed tool, called Multi Energy Systems Simulator (MESS), is a modular, multi-node model that allows to investigate non optimal solutions by simulating the energy system. The model has been built having in mind urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be be represented as well. MESS is capable of performing analysis on systems composed by multiple energy carriers (e.g. electricity, heat, fuels). In this work, the tool’s features will be presented by a comparison between MESS itself and an optimization model, in order to analyze and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development.

Due to their increased surface area, extent of swelling and active substance loading capacity and flexibility, nanogels made from natural and synthetic polymers have gained significant interest in the scientific and industrial areas. Especially, customized design and implementation of non-toxic, biocompatible, and biodegradable micro/nano carriers makes their usage very feasible for a range of biomedical applications, including drug delivery, tissue engineering, and bioimaging. The design and application methodologies of nanogels have been outlined in this review. Additionally, the most recent advancements in nanogel biomedical applications have been discussed, with a particular emphasis on applications for the delivery of drugs and biomolecules.

Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.

Zoos are increasingly putting in place formalized animal welfare assessment programs to allow monitoring of welfare over time, as well as to aid in resource prioritization. These programs tend to rely on assessment tools that incorporate resource-based and observational animal- focused measures since it is rarely feasible to obtain measures of physiology in zoo-housed animals. A range of assessment tools are available which commonly have a basis in the Five Domains framework. A comprehensive review of the literature was conducted to bring together recent studies examining welfare assessment methods in zoo animals. A summary of these methods is provided with advantages and limitations of the approach es presented. We then highlight practical considerations with respect to implementation of these tools into practice, for example scoring schemes, weighting of criteria, and innate animal factors for consideration. It is concluded that would be value in standardizing guidelines for development of welfare assessment tools since zoo accreditation bodies rarely prescribe these. There is also a need to develop taxon or species- specific assessment tools to inform welfare management.

As the cost of conventional energy continues to rise, and concerns over the environment grow, there is a growing interest in sustainable energy. It is believed that conventional energy will only last for around 50 years, making it essential to find a renewable energy source that is safe and feasible to replace fossil fuels. Solar energy is a superior alternative to conventional energy, as it is renewable and produces green energy that can be used directly. The maritime industry has found itself at the forefront of an evolving environmental consciousness, driven by the imperative to mitigate the carbon footprint left in the wake of ships. In this journey towards sustainability, solar energy has emerged as a beacon of hope, harnessing the boundless energy of the sun to wean vessels off their dependence on fossil fuels. Technological advancements have made solar power more accessible, and we can now see the use of solar panels on large ships and small crafts to reduce fuel usage and emissions. Solar panels are also increasingly used to power homes and businesses, and many countries are implementing policies to encourage their use. Solar energy is a key solution in reducing our dependence on non-renewable energy sources and combating climate change. However, the adoption of solar energy is but a stepping stone towards maritime sustainability, as the integration of CO2 capture, utilization, and storage (CCUS) technologies promises to amplify the industry's environmental impact to unprecedented heights.

Energy imbalance gap (EIG) is defined as the average daily difference between energy intake (EI) and energy expenditure (EE). This study aimed to examine the associations between EIG and sociodemographic and anthropometric variables in the adolescent population of eight Latin American countries. A total of 680 adolescents aged 15 to 18 were included in this study. EI was estimated using two non-consecutive 24-hour dietary recalls. EE was predicted from Schofield equations using physical activity levels obtained through the long version of the International Physical Activity Questionnaire. Sociodemographic data and anthropometric measurements were also obtained. A descriptive analysis and multilevel linear regression models were used to examine associations between variables. The mean EI, EE, and EIG were 2091.3 kcal, 2067.8 kcal, and 23.5 kcal, respectively. Argentina and Colombia had the highest EI and EIG, whereas Chile and Costa Rica had the lowest EI and EIG. Males had a higher EI (2262.4 kcal) and EE (2172.2 kcal) than females (1930.1 kcal and 2084.5 kcal), respectively (p<0,05). Overweight subjects had a lower EIG than did underweight and normal-weight subjects (p<0,05). Subjects with high SES had a lower EE (2047.0 kcal) than those with low SES (1963.7 kcal) (p<0,05). Sex and BMI were associated with EIG in adolescents from Latin America.

A comprehensive Energy Audit of a Maintenance facility was performed to assess its energy performance and identify scope for improvement. The facility’s Energy Use Intensity (EUI) for 2022 was 404 kWh/m2 — more than double the Benchmark EUI for Maintenance facilities (151 kWh/m2) recommended by EnergyStar. Furthermore, the Load Factor for 2022 was 0.22, which is lower than the recommended minimum of 0.75 for an efficient building. The audit encompassed an in-depth evaluation of the building's structural and operational characteristics, comprising HVAC systems, lighting, building envelope, and energy-intensive machinery. An energy model of the building was developed to emulate the baseline building energy performance for 2022. Following the energy model's development and validation, an analysis was conducted to identify energy-intensive areas and opportunities for optimization. Energy Efficiency Measures were then formulated, focusing on improving energy efficiency while focusing on energy consumption reduction and GHG emission reduction. Results demonstrated the potential of Energy Audits and Modeling to enable significant reductions in energy consumption and promote sustainable building practices. Among the Energy Efficiency Measures considered, re-sizing and decarbonizing HVAC equipment contributed the most to energy savings, with a 100% decrease in natural gas and 37% decrease in electricity use annually.

The analysis presented is based on actual GHU energy demand. The special feature of this load is the existence of strong daily fluctuations in energy consumption, especially during the summer months. This study presents a methodology for managing such loads with the main objective of maximizing the operating efficiency of MG.

In the last 20 years, the share of renewable energy sources in the production of electricity in the European Union has doubled, from about 15% to almost 35%. The main driver of this development has been the increase in the share of wind energy and solar photovoltaic energy. The authors aim to analyze the influencing factors that affect the energy transition process applied to nautical tourism, from polluting energy to renewable solar energy. The authors' research approach consists in using the framework offered by the energy transition process from the perspective of the socio-technical and economic approach, by applying the qualitative research method with a deductive approach. The tool used to achieve the objective was the semi-structured interview. The research unitarily, holistically, and specifically approaches the problem of energy transition from polluting sources to renewable ones offered by solar energy, in the case of nautical tourism with direct implications on the specific industry in the Netherlands. The research results are structured in the fields of technology, governance, economics, and user preferences. This research has the potential to provide support to find optimal solutions that encourage users to accelerate the energy transition process by adopting sustainable solutions for nautical tourism.

Sustainability of current energy policies and known mid-term policies are analised in their multiple facets. First an overview is given about the trend of global energy demand and energy production, analysing the share of energy sources and the geographic distribution of demand, on the basis of statistics and projections published by major agencies. The issue of sustainability of the energy cycle is finally addressed, with specific reference to systems with high share of renewable energy and storage capability, highlighting some promising energy sources and storage approaches.

Since the requirements in terms of power of the electronic applications range wide, the developed Energy Harvesting (EH) systems limit their availability to the less power demanding applications. However, this paper focuses on increasing the energy levels collected in the EH system so that it can be included in more demanding applications in terms of power. Therefore, an electronic system capable of grouping many single harvesting channels into one single system is analyzed in this paper. This multi-harvester electronic system is able to manage efficiently the energy collected by multiple vibratory transducers. The paper includes a comparison of its performance against some of the State-of-the-Art EH energy management circuits that interface the transducers. The method employed to demonstrate the intrinsic efficiency of each of the electronic circuits tested was based on experimental tests, where the average power transferred from several identical and simultaneous electric sources to a single storage element was measured. It was found out that only one energy management circuit was able to increase the transferred energy in a linear way while new input electric sources were added.

The global energy transition is motivated largely by the need to combat climate change. Other reasons include limits to available fossil fuels, energy-related environmental impacts of many kinds, and societal goals such as equity and providing sustainable energy for the poor. This transition will significantly change the world both technically, economically and socially. It is taking shape with characteristics which prioritise certain types of policy, interventions and solutions, as well as certain actors and institutional forces. More fundamentally, it embodies underlying assumptions about what constitutes a desirable type of economy and society. Briefly stated, the energy transition is being geared to a conventional economic growth-focused type of development; including the not unchallenged concept of green growth. Not everyone, however, would agree on those priorities or the underlying assumptions. We point out various constraints and limitations which this approach places on the energy transition. Whilst much of the change being initiated is positive, we argue that different views, which lie closer to discourses such as strong sustainability (1), deep ecology (2) and ecological economics (3, 4) might prioritise differently and offer both necessary and desirable outcomes. Given the predominant focus on energy and climate, equally essential goals such as equity and eradication of poverty appear to be at particular risk of being missed. Building on examples and recent experience we argue that achieving global sustainable energy, not least the societal goals, remains unlikely within socio-political frameworks permeated by the imperatives of market-driven growth, the commodification of resources, and intense competitive dynamics. Technology and economics, the themes of this issue, have far-reaching societal implications and outcomes. We argue that it is essential that they be considered within the broad context discussed here.

Herein we report the expansion of the chemical space available from the chitin, accessible via the biogenic N-platforms 3A5AF, M4A2C and di-HAF. The biologically active heteroaromatics furo[3,2-d]pyrimidin-4-one and furo[3,2-d]pyrimidin-4-amine can be selectively accessed from 3A5AF and M4A2C, respectively. The chiral pool synthon di-HAF is a viable substrate for the Achmatowicz rearrangement, providing streamlined access to 2-aminosugars possessing a versatile hydroxymethyl group at C5.

Advancements in Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) have opened new opportunities for architects and engineers working on complex buildings, such as offices. These developments allow designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. To address conflicting objectives that arise during the design process, genetic algorithms with multi-objective optimization (MOO) have been employed in architectural design. The GENIUS project aims to optimize building shape, apertures, and shading systems in the concept and design stages, with a focus on energy and daylight performance. The project integrates BIM and visual programming tools with Artificial Intelligence techniques like Genetic Algorithms and RBFOpt model-based optimization. Optimization algorithms are used to identify the best solutions that meet all design objectives, helping architects optimize their designs and achieve desired outcomes. The workflow was tested on a case study of a large office building, with MOO focused on maximizing daylight performance using Spatial Daylight Autonomy metric and minimizing energy consumption using Energy Use Intensity metric. The project provides architects with a method to improve their designs using Algorithm Aided Design tools, which help identify the best solutions for complex design problems.

Paper presents the energy policy of the Republic of Serbia with special attention to the energy situation on the government controlled territory. South Serbian autonomous province of Kosovo and Metohija is under UN jurisdiction since the 1999 according to UNSC Resolution 1244. Renewable energy sources are rarely used in Serbia with exception of energy from hydropower plants, but in this sector priorities in geothermal and energy coming from biomass recently increased. In natural gas sector, Serbia has the deal with Russia for construction of South Stream gas-line through Serbia and for construction of the first underground storage in depleted gas reservoir in Banatski Dvor. In 2008, Serbia also sold 51% of the government founded petroleum industry – NIS which has exclusive monopoly for exploitation of crude oil. Serbian government has complete monopoly in electric power sector. Electric power infrastructure became technologically obsolete, and operative efficiency is at very low level. Serbia has not yet decided whether Serbian Electric Power Industry – EPS will be privatized. District heating sector mostly natural gas fuelled is highly inefficient and it is in jurisdiction of local municipalities but also has social component dictated by central government.

The evolution of photovoltaic cells is intrinsically linked to advancements in the materials from which they are fabricated. This review paper provides an in-depth analysis of the latest developments in silicon-based, organic, and perovskite solar cells, which are at the forefront of photovoltaic research. We scrutinize the unique characteristics, advantages, and limitations of each material class, emphasizing their contributions to efficiency, stability, and commercial viability. Silicon-based cells are explored for their enduring relevance and recent innovations in crystalline structures. Organic photovoltaic cells are examined for their flexibility and potential for low-cost production, while perovskites are highlighted for their remarkable efficiency gains and ease of fabrication. The paper also addresses the challenges of material stability, scalability, and environmental impact, offering a balanced perspective on the current state and future potential of these material technologies.

The aim of this study was to investigate the impact of Process Oriented Guided Inquiry Learning (POGIL)-based instruction versus lecture-based instruction on Grade 12 students’ performance in circular motion unit. A quasi-experimental, pretest-posttest design, the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes: Knowing, Applying and Reasoning (KAR). The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys) and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a unit of circular motion in physics using POGIL-based instruction while the control group was taught the unit using lecture-based instruction. The findings of the study showed statistically significant differences between students of the control group and the treatment group in favor of the later regarding their circular motion performance, suggesting the superiority of the POGIL in enhancing student understanding of the circular motion.

This article investigates the prospect of implementing multidisciplinary and multicultural student teamwork (MMT) involving Case-based Learning (CBL) and Problem-based Learning (PBL) as a sustainable teaching practice. Based on a mixed methods approach, which includes direct observation (both physical and virtual), questionnaire distribution and focus-group interviews the study reveals that MMT through CBL and PBL can both facilitate and hinder sustainable learning. Our findings show that while MMT enhances knowledge sharing, it also poses a wide range of challenges, raising questions about its social significance as a sustainable teaching practice. The study suggests the implementation of certain mechanisms, such as ‘Teamwork Training’ and ‘Pedagogical Mentors’, aiming to strengthen the sustainable orientation of MMT through CBL and PBL.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

This article discusses the use of game design as a method for interdisciplinary project-based teaching in secondary school education to convey informatics and society topics. There is a lot of knowledge about learning games but little background on project-based teaching using game design as a method. We present the results of an analysis of student-created games and an evaluation of a student-authored database on learning contents found in commercial off-the-shelf games. We further contextualise these findings using a group discussion with teachers. Results underline the effectiveness of project-based teaching to raise awareness for informatics and society topics. We further outline informatics and society topics that are particularly interesting to students, genre preferences and potentially engaging game mechanics stemming from our analyses.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

The integration of the increasing share of Renewable Energy Sources (RES) requires the availability of suitable energy storage systems to improve the grid flexibility, and Compressed Air Energy Storage (CAES) systems could be a promising option. In this study, a CO2-free Diabatic CAES system is proposed and analysed. The plant configuration is derived from a down-scaled version of the McIntosh diabatic CAES plant, where the natural gas is replaced with green hydrogen, produced on site by a Proton Exchange Membrane electrolyser powered by a Photovoltaic power plant. In this study, the components of the hydrogen production system are sized to maximize the Self-Consumption share of PV energy generation and the effect of the design parameters on the H2-CAES plant performance are analysed on a yearly basis. Moreover, a comparison between the use of natural gas and hydrogen in terms of energy consumption and CO2 emissions is discussed. The results show that the proposed hydrogen fuelled CAES can effectively match the generation profile and the yearly production of the natural gas fuelled plant by using all the PV energy production, while producing zero CO2 emissions.

“Energy” is probably the most important term in science and technology. The reason is energy can be converted while it is overall conserved, and thereby correlates changes of a wide variety. Despite its successful use in many areas over more than a century, an analysis of current literature shows that definitions of what energy is and explanations why it is conserved are inconsistent. With regard to energy forms not even a definition was found, and examples given are often wrong. Looking at two interacting objects in the most simple situations, with energy conservation but without conversion, a consistent understanding of energy and its conservation can be derived. Based on it, the systematic set of energy forms with a clear definition of each is then presented.

In this paper, solar irradiance and wind speed forecasts were performed considering time horizons ranging from 10 min to 60 min, under a 10 min time-step. Global horizontal irradiance (GHI) and wind speed were computed using four forecasting models (Random Forest, k-Nearest Neighbours, Support Vector Regression, and Elastic Net) to compare their performance against two alternative dynamic ensemble methods (windowing and arbitrating). Forecasting models and dynamic forecasting ensembles were implemented in Python for performance evaluation. The performance comparison between the prediction models and the dynamic ensemble methods was carried out by evaluating the RMSE, MAE, R² and MAPE, to evaluate whether the dynamic ensemble forecasting method obtained greater. According to the results obtained windowing dynamic ensemble method was the most efficient among the tested. For the wind speed data, by varying its parameter λ (from 1 to 100), a variable performance profile was obtained, where from λ =1 to λ = 74, windowing proved to be the most efficient, reaching maximum efficiency for λ = 19. Windowing was the best method for the GHI analysis, reaching its best performance for λ = 1. The efficiency gain using windowing was 0.56% when using the wind speed model and 1.96% for GHI.

A Life Cycle Analysis was performed considering three existing power plants of comparable size operating with different sources of renewable energy: geothermal, solar and wind. Primary data were used for building the life cycle inventories. The geothermal power plant includes emissions treatment for removal of hydrogen sulfide and mercury. The scenario about the substitution of natural emissions from geothermal energy, with specific reference to the greenhouse effect, is also investigated performing a sensitivity analysis. The results are characterized employing a wide portfolio of environmental indicators employing the Recipe 2016 and the ILCD 2011 Midpoint+ methods; normalization and weighting are also applied using the Recipe 2016 method at endpoint level. The results demonstrate a good eco-profile of geothermal power plant with respect to other renewable energy systems and allow for a critical analysis to support potential improvements of the environmental performances.

In the Kingdom of Saudi Arabia (KSA), residential buildings’ energy consumption accounts for almost 50% of the building stock electricity consumption. The electricity generation consumes over one-third of the daily oil production. KSA was ranked as one of the highest countries in fossil fuel consumption per capita in 2014. Moreover, the KSA’s economy heavily relies on fossil fuel sources, namely oil reservoirs, whereby depletion will negatively affect the future development of the country. The total electricity consumption is annually growing by approximately 5-8%, which would lead to identical oil consumption to oil production in 2035. Currently, the KSA government is concerned to generate more renewable energy using large renewable energy plants. The government is investing in energy generation through renewable sources, by financing large scale photovoltaic farms to stop an economic crisis that may occur in 2035. The existing building stock consumes around 80% of the total current Saudi electricity that is generated. According to the Saudi energy efficiency report, the primary energy consumption per capita is over three times higher than the world average. Therefore, the residential buildings need further assessment as to their current energy consumption. This research used a survey to explore current user behaviour in residential buildings energy performance in the city of Jeddah, KSA. The findings of the survey showed: • The buildings thermal properties were found to be poorly designed • The majority of users within the buildings prefer a room temperature of below 24 °C, which requires a massive amount of cooling • Due to the climate conditions and the cultural aspects of KSA, housing units are occupied for more than 18 hours per day • An increase in user awareness has helped to slightly improve residential buildings energy efficiency Knowing the current high energy consumption sources and causes and being able to define available opportunities for further developments on building thermal properties enhancements and how to increase user awareness to reach self-sustaining buildings is essential.

An energy diagnosis is a tool used to seek the improvement of energy saving measures, environmental conservation and energy efficiency, making relevant its implementation in any kind of buildings. For this article, an energy diagnosis of third level was carried out in buildings of the Instituto de Energías Renovables (IER) from Universidad Nacional Autónoma de México (UNAM) through survey and census of the 36 buildings in the IER, in order to characterize current patterns of energy consumption and demand, and generating specific strategies towards savings and energy efficiency, such as indicators and corrective proposals within and non-financial investment.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

Energy efficiency is, in principle, a simple idea: an output of human value, for example, vehicle-km traveled, divided by the needed input energy. Efficiency improvements are regarded by many as an important means of mitigating not only climate change, but also other environmental problems. Accordingly, many countries have efficiency ratings for appliances and efficiency standards for road vehicles. Despite the vast number of articles published on energy efficiency, few question whether it is a useful or accurate measure in its present form. This review addresses this lack, by a critical review of the literature, not only in energy efficiency, but in other areas of research, such as ‘energy services’, that can help broaden the scope of this idea, both geographically and conceptually. These shortcomings are illustrated in case studies of road passenger transport and buildings. The main findings are that energy efficiency inevitably has an ethical dimension, that feedbacks are more widespread than generally considered, and that conventional efficiency measures omit important energy input items, particularly those concerned with mining of the materials needed for renewable energy plants. Finally, the key results of this review are summarized, and its limitations are discussed, as is the future research needed to overcome these shortcomings.

Hospital-based programs democratize mRNA therapeutics by facilitating the processes to translate a novel RNA idea from the bench to the clinic. Because mRNA is essentially biological software, therapeutic RNA constructs can be rapidly developed. The generation of small batches of clinical grade mRNA to support IND applications and first-in-man clinical trials, as well as personalized mRNA therapeutics delivered at the point-of-care, is feasible at a modest scale of cGMP manufacturing. Advances in mRNA manufacturing science and innovations in mRNA biology, are increasing the scope of mRNA clinical applications.

Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals.

Drug discovery utilizes high-throughput screening (HTS) methods applying target and cell-based assays. This review discusses the challenges and benefits associated with these assays in HTS. Discussed the strategies that can be applied for the development of screening assays through primary and secondary screens for target identification. Further discussion on identify-ing the most efficacious drugs following these approaches in cancer. Even though various drugs have been identified to treat cancer, there is high demand of more relevant phenotypic assays to produce desired diseased phenotype to only highlighting the specific targets instead of off-targets. Hopefully, the new developing strategies could provide innovative drugs to treat cancer patients to reduce mortality rate.

The correlations of seismic noise, recorded during 1997-2022 at 229 stations around the world are investigated. The property of waveforms, known as the Donoho-Johnston threshold, is analyzed, which separates the absolute values of the orthogonal wavelet coefficients into "small" and "large". Network of 50 reference points is considered. For each reference point, a time series with a time step of 1 day is calculated as the median of the values of the DJ index from the 5 nearest stations. For each reference point, the correlations between the values of the DJ index at other reference points are calculated in a sliding time window of 365 days. Average values of the DJ index decrease, which is interpreted as indicators of global seismic hazard increasing. The maximum distances between reference points with strong correlations show an explosive increase after 2011. Bursts in the mean coherences between the length of day (LOD) and the values of the DJ index at the reference points precede the release of seismic energy with a delay of about 530 days. High amplitude of the DJ index response to LOD for the times 2020-2022 was identified, which may forecast a strong earthquake in the second half of 2023.

Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

Animal-based measures are the measure of choice in animal welfare assessment protocols as they can often be applied completely independently to the housing or production system employed. Although there has been a small body of work on potential animal-based measures for farmed crocodilians [1-3], they have not been studied in the context of an animal welfare assessment protocol. Potential animal-based measures, that could be used to reflect the welfare state of farmed crocodilians, were identified and aligned with the Welfare Quality® principles of good housing, good health, good feeding and appropriate behaviour. A consultation process with a panel of experts was used to evaluate and score the potential measures in terms of validity and feasibility. This resulted in a toolbox of measures being identified for further development and integration into animal welfare assessment on the farm. Animal-based measures related to ‘good feeding’ and ‘good health’ received the highest scores for validity and feasibility by the experts. There was less agreement on the animal-based measures that could be used to reflect ‘appropriate behaviour’. Where no animal-based measures were deemed to reliably reflect a welfare criterion nor be useful as a measure on the farm, additional measures of resources or management were suggested as alternatives. Future work in this area should focus on the reliability of the proposed measures and involve further evaluation of their validity and feasibility as they relate to different species of crocodilian and farming system.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

Given the increase in consumers’ preferences for coffee, it is becoming important to understand their decision-making processes in the coffee chain context. To deepen the understanding of sustainable outcomes in this context, this study investigates the role of dedication- and constraint-based mechanisms in forming consumers’ repurchase and positive word-of-mouth (WOM) intentions, two critical sustainable outcomes. We examined the effects of coffee quality, the quality of the physical environment, and service quality in accelerating the formation of dedication-based factors. Moreover, this study offers an in-depth understanding of the enablers of perceived switching costs. Data collected from 238 university students that frequently visit coffee chains are empirically tested against the proposed theoretical framework by using structural equation modeling. The results confirm that both dedication- and constraint-based factors substantially predict consumers’ sustainable outcomes in the coffee chain context. Brand image and perceived switching costs play an important role in enhancing consumers’ repurchase and positive WOM intentions compared with customer satisfaction. Coffee quality is significantly associated with both customer satisfaction and brand image, whereas the quality of the physical environment and service quality are only significantly associated with brand image. Habit is found to be the key enabler of perceived switching costs, while loyalty programs have no significant impact on perceived switching costs.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

In this study, we propose a module-type triboelectric nanogenerator (TENG) capable of harvesting power from a variety of mechanical energy sources. The potential energy and kinetic energy of water are used for the rotational motion of the generator module, and electricity is generated by the contact/separation generation mode between the two triboelectric surfaces inside the rotating TENG. Through the parametric design of the internal friction surface structure and mass ball, we optimized the output of the proposed structure. To magnify the power, experiments were conducted to optimize the electrical output of the series of TENG units. The electrical signal generated by the module-type TENG can be used as a sensor to recognize the strength and direction of various physical quantities, such as wind or earthquake vibrations.

Solar photovoltaic (PV) technology is a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. This paper explores the pivotal role of PV technology in re-ducing greenhouse gas emissions and combatting the pressing issue of climate change. At the heart of its efficacy lies the efficiency of PV materials, which dictates the extent to which sunlight is transformed into electricity. Over the last decade, substantial advancements in PV efficiency have propelled the widespread adoption of solar PV technology on a global scale. The efficiency of PV materials is a critical factor, determining how effectively sunlight is transformed into electricity. Enhanced efficiency, achieved through a decade of progress, has driven the global expansion of solar PV. Multi-junction photovoltaic materials have now exceeded 40% efficiency in lab tests. China leads the world in solar PV installations, boasting over 253 GW of installed capacity by the end of 2021. Other prominent countries in this sector are the United States, Japan, Germany, and India. Supportive policies like feed-in tariffs, net metering, tax incentives, and cost reductions in PV modules have made solar PV increasingly competitive against fossil fuel-based power generation. Solar PV technology holds immense potential for creating a cleaner, reliable, scalable, and cost-effective electricity system. To expedite its deployment and foster a more sustainable energy future, continued investment in research and development, along with supportive policies and market mechanisms, is essential. This paper underscores the pivotal role of solar PV technology in the global energy transition and advocates for a concerted effort to unlock its full potential in achieving a more sustainable and resilient energy future.

The quickest and easiest way to avoid greenhouse gas (GHG) emissions is to purchase renewable electricity and offset the remaining emissions. However, the industrial sector’s electricity needs already exceed renewable electricity generation. Moreover, electricity accounts for only one third of the industry’s energy needs. Simultaneously, the advance of sectoral coupling and the decarbonisation of industrial processes, as well as the desire to rapidly decrease dependence on fossil fuels, are creating significant additional demand for renewable energy. Neither existing nor planned generation and transmission infrastructure will suffice to meet the expected short-term demand. Based on survey data from the German Industry Energy Efficiency Index, this article therefore examines the share of GHG savings that companies intend to achieve on- and off-site. Understanding how much additional generation and transmission capacity is needed by the industry to decarbonise and by when is crucial to identify and address the extent of excess demand. On average, companies plan to avoid 22 % of their 2019 emissions by 2025 and 27 % by 2030, primarily through on-site measures. In combination with the extrapolation of the entire industry’s needs for off-site capacity, the data calls for a rapid expansion of planning authority and green generation capacities.

This article has been developed to assess the economic feasibility of a roof-top photovoltaic installation of industrial self-consumption. Numerical models that enable an interested person to obtain the main expected parameters will be generated. To do this, a calculation methodology will be generated through which the reader, knowing the location of the facility and dimensions of the roof, will be able to calculate the maximum installable power, the main parameters related to production, the cost of the installation, and the LCOE of the plant. The use of actual costs will be facilitated in case they are known, but it will remain possible to apply the costs of the major equipment (modules, inverter, and structure) considered throughout the article. This developed calculation methodology will also allow a quick comparison of the forecasts of production, CAPEX, and LCOE of plants designed with different inclinations and different types of modules. Consequently, it will be especially useful for decision-making before developing the plant's basic engineering. Moreover, the calculations used for modeling the LCOE will be analyzed in depth. This analysis will allow evaluating how the different technical variables affect the profitability of a photovoltaic installation, such as the selected tilt, the location, the module's technology, or the available area.

The building sector accounts for nearly 40% of total primary energy consumption in the U.S. and E.U. and 20% of worldwide delivered energy consumption. Climate projections predict an increase of average annual temperatures between 1.1-5.4°C by 2100. As urbanization is expected to continue increasing at a rapid pace, the energy consumption of buildings is likely to play a pivotal role in the overall energy budget. In this study we used EnergyPlus building energy models to estimate the future energy demands of commercial buildings in Salt Lake County, Utah, USA, using locally-derived climate projections. We found significant variability in the energy demand profiles when simulating the study buildings under different climate scenarios, based on the energy standard the building was designed to meet, with reductions ranging from 10% to 60% in natural gas consumption for heating and increases ranging from 10% to 30% in electricity consumption for cooling. A case study, using projected 2040 building stock, showed a weighted average decrease in heating energy of 25% and an increase of 15% in cooling energy. We also found that building standards between ASHRAE 90.1-2004 and 90.1-2016 play a comparatively smaller role than variation in climate scenarios on the energy demand variability within building types. Our findings underscore the large range of potential future building energy consumption which depend on climatic conditions, as well as building types and standards.

The conceptual reconstruction of Neiwan powerhouse is one of the key activities under the current ongoing mapping project of Taiwanese hydropower plants that mainly took place between 2013 and 2015 and is now focused on micro, pico, and historical power plants. Judging from the fact that the oldest hydropower plant in Taiwan named Guishan starts its operation in 1905, Neiwan powerhouse was among the very first powerhouses that were built across the island to support the electrification of Taiwan. However, the main function of the single turbine equipped Neiwan micro powerhouse was to support mainly the military needs and protect the territories occupied by Japanese troops. Since the powerhouse was built in 1909 and operates only something about 10 year there are very little physical materials or evidence along with contemporaries. Therefore the further reconstruction is based mainly on physical observation of the remains located at the site, old photographs, related articles, treatises and typology of mechanical and civil constructions of other hydropower plant cases in Taiwan hence this paper´s main intention is to pitch a concept reconstruction rather than definite conclusion.

This research paper is part of the wider project concerning the very first detailed mapping of the overall Taiwanese hydro-power plants that took place from 2013 up to 2015 and it is currently in evaluation and finalization stage. The case of Shanping hydro-power plant has been carefully studied, photographed, documented and mapped in situ. It was one of the isolated hydro-power plant projects originally built to supply the remote area with the specific designation. Shanping hydro-power plant, as well as the other units from the early hydro-power generation era in Taiwan, are considered to be the technological heritage of civil and mechanical engineering that reflects later in all the further projects up to nowadays modern Taiwanese hydro-power plants. Unfortunately, most of the hydro-power houses from the older periods were severely damaged or destroyed by natural causes which were also the case of Shanping unit. The research is trying to reconstruct the original location of the powerhouse and its supporting structures based on available historical documents, previous studies, comparative methodology, and the current on-site observation.

The current energy policy recommends the idea of energy efficiency over fossil energy as a primary matter for the coming years. The kingdom of Morocco requires restructuring of its power equipment by increasing the percentage of renewable energy supplies, optimizing their systems and power storage. Therefore, increasing energy efficiency is an as important obligation as reducing the overall energy consumption. The purpose of this research is to present the energy transition in Morocco towards renewable energies and to assess the diversity of available marine natural resources. Recent research in conversion of ocean thermal energy, wave energy, tidal energy, offshore wind energy, and osmotic energy into power supply has started to encourage different technologies. This research has led to commercial deployment in some cases such as our 550 km long Mediterranean coast and 3000 km long Atlantic. This does not only result in fossil energies independency but also provides advantages like less cost and no pollution.

Many research work has demonstrated that taken the Combined Cooling Heating and Power system (CCHP) as the core equipment, the integrated energy system (IES) can bring obvious benefit to energy efficiency, CO2 emission reduction and operation economy in urban areas. Compared with isolated IES, integrated energy micro-grid (IEMG) which is formed by connecting multiple regions IES together, through distribution and thermal network, can further improve the reliability, flexibility, cleanliness and economy of regional energy supply. Based on the existing IES model, this paper describes the basic structure of IEMG and built a IEMG planning model. The planning based on the mixed integer linear programming, and economically construction planning scheme are calculated by using known electricity, heating and cooling loads information and the given multiple equipment selection schemes. At last, the model is validated by a case study. The results show that the application of IEMG can effectively improve the economy of regional energy supply.

Saudi Arabia is an oil-reliant nation as a large percentage of its GDP comes from oil resources. Oil dependency leaves a county at the mercy of the international crude market, and a decrease in the price of crude can seriously destabilize the economy of such nations. An example is the case of Venezuela whose dependence on oil caused a national disaster (McCarthy, 2017). As such, the nation’s exports, GDP, and government revenue are primarily dependent on oil revenue, and the recent decrease in the oil prices has decreased Venezuela’s national revenue resulting in economic collapse as well as inflation. A shift from a resource based economy to a knowledge based economy will help Saudi Arabia become less reliant on its oil revenues for its economic stability and growth (Nurunnabi, 2017).