Wind energy is becoming an essential source of power for countries which have the aim to reduce greenhouse gases emission and mitigate the effects of global warming. The Wind Turbines (WTs) installed around the globe is increasing significantly every year. The dramatic increase in wind power has encountered quite a few challenges, among which the major issues are availability and reliability. The unexpected failure in WTs Gearbox (GB) ultimately increases the Operation and Maintenance (O&M) cost. The identification of faults in the earlier stages before it turns to catastrophic damage to other components of WT is crucial. This research deals with the prediction of WT failures by using a Supervisory Control and Data Acquisition (SCADA) system. The main aim is to forecast the temperature of the WTs GB to predict the impending overheating of the GB at an early stage. The earlier prediction will help to optimize the maintenance period and to save maintenance expenses and, even more important, generate warnings in due time to avoid major damages or even technical disasters. In the proposed method we compared six different machine learning (ML) models based on error and accuracy of prediction. The bagging regressor is the best ML model, which results in the mean square error of 0.33 and R of 99.8 on training data. The bagging regressor is then used to predict the fault in the WT GB, which detected the anomalous behavior of WT GB 59 days earlier than the actual failure. This model also detects the extremely unusual behavior of the GB 9 days earlier than a complete failure.

In the present study, we compare energy transition scenarios from a new set of Integrated Assessment Models, the suite of MEDEAS models, based on a systems dynamic modelling approach, with scenarios from two already well know structurally and conceptually different Integrated Assessment Models, the Integrated MARKAL-EFOM System (TIMES) and the Long-range Energy Alternatives Planning system (LEAP). The investigation was carried out to cross-compare and benchmark the response of MEDEAS models with TIMES and LEAP in depicting the energy transition in two different countries, Austria and Bulgaria. The preliminary results show a good agreement across all the models in representing scenarios projecting historical trends, while a major discrepancy is detectable when the rate of implementation of renewable energy is forced to increase to achieve energy system decarbonization. The discrepancy is mainly traceable to the differences in the models’ conception and structures rather than in a real mismatch in representing the same scenarios. The present study is put forward as a guideline for validating new modelling approaches that link energy policy decision tools to the global biophysical and socioeconomic constraints.

Tidal stream energy, due to its high level of consistency and predictability, is one of the feasible and promising type of renewable energy for future development and investment. Applicability of Blade Element Momentum (BEM) method for modeling the interaction of turbines in tidal arrays has been proven in many studies. Apart from its well-known capabilities, yet there is scarcity of research using BEM for the modeling of tidal stream energy farms considering full scale rotors. In this paper, a real geographical site for developing a tidal farm in the southern coasts of Iran is selected. Then, a numerical methodology is validated and calibrated for the selected farm by analyzing array of turbines. A linear equation is proposed to calculate tidal power of marine hydrokinetic turbines. This methodology narrows down the wide range of turbine array configurations, reduces the cost of optimization and focuses on estimating best turbine arrangements in a limited number of positions.

Due to the fact that irrigation networks are water and energy-hungry and that both resources are scarce, many strategies have been developed to reduce this consumption. Otherwise, solar energy sources have become a green alternative with lower energy costs and, as a consequence, lower environmental impacts. In this work, it is proposed a new methodology to select the scheduled program for irrigation which minimizes the number of photovoltaic solar panels to be installed and which better fits energy consumption (calculated for discrete potential combinations; using a programming software to assist) to available energy obtained by panels without any power conditioning unit. So, the irrigation hours available to satisfy the water demands are limited by sunlight, the schedule type of irrigation has to be rigid (rotation predetermined) and the pressure at any node has to be above the minimum pressure required by standards. A real case study has been performed.

Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, during the charging and the discharging process, there are some parameters that are not controlled by the user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Different control methods have been developed with the goal of protecting the battery and extending its life expectancy, being the most used the constant current-constant voltage. However, several studies show that charging time can be reduced by using Fuzzy Logic Control or Model Predictive Control. Other benefits are; temperature control and an extension of life expectancy. For all these reasons, FLC and MPC have proven to be more efficient than traditional charge control methods.

As the leader of the largest economy, President of the United States has substantive influence on addressing the global climate change problem. However, presidential election is often dominated by issues other than energy problems. This paper focuses on the on-going 2016 presidential election, examining the energy plans proposed by the leading Democrat and Republican candidates. Our data from the Iowa caucus survey in January 2016 suggests that voters are more concerned about terrorism and economic issues than environmental relative issues. We then compare the Democratic and Republican candidate’s view of American’s energy future, and evaluate their proposed renewable energy targets. We find that the view on renewable energy is polarized between Democratic and Republican candidates, while candidates from both parties agree on the need for energy efficiency. Results from our ordinal least squares regression models suggest that Democratic candidates have moderate to ambitious goals for developing solar and other renewable energy. The Republican candidates favor fossil fuel and they neglect to provide any plan for renewable energy. In addition, this trend of polarization has grown more significant when compared with the past three presidential elections. Our observation suggests that energy issues need to be discussed more to draw broader attention to salient issues of diversifying and decarbonizing the nation’s energy system.

Driven by environmental sustainability concerns, the integration of biobased components in curtain wall systems is gaining traction in both research and the construction market. This paper explores the development and validation of a bio-based façade system within the Basajaun H2020 project. The project aimed to demonstrate the feasibility of introducing environmentally friendly bio-based components into the mature curtain wall façade industry. The paper focuses on identifying technological solutions for replacing key components such as profiles, insulation, and the tightness system with bio-based and less environmental impact alternatives presenting the results achieved in the façade system design of the Basajaun project funded by European Union. These solutions aimed at creating a bio-composite-based curtain wall façade that adheres to current building envelope standards and normative; to implement diverse façade typologies for vision panels, opaque sections, and integrated windows; to engineer the prefabrication process for industrialization, enabling wider market replication and simplified transport and installation. The results demonstrate that the Basajaun façade successfully integrates selected components and meets the performance requirements set by regulations. However, questions remain regarding the workability of bio-based profiles as a commercially viable, ready-to-market solution that can replace traditional aluminum profiles in the curtain wall façade industry.

This review aims to consolidate varied information regarding Port Scanning, and examine the tools, techniques, and detection algorithms used. It explores TCP SYN, Full TCP, UDP, and stealth scans, and shows their functions and abilities. Paired with these techniques, it also shows the tools that they can be used by, such as NMap, Zmap, and MASSCAN. It discusses the various detection techniques such as Signature and Anomaly based detection, and discusses the real world impact of this technology, while commenting on the legal and ethical issues posed.

This study reports the synthesis and characterization of novel per-fluorinated organic polymers with azo- and azomethine-based linkers using nucleophilic aromatic substitution. The polymers reveal variations in the fluorine content via the incorporation of decafluorobiphenyl and hexafluorobenzene linkers, which enhanced their hydrophobic nature. The rich fluorine polymers were slightly soluble in THF and have shown molecular weights between 4886 to 11948 g/mol. All polymers exhibit thermal stability in the range of 350-500°C, with varying thermal stability depending on the fluorine and nitrogen content. Conjugation of the polymers was confirmed through changes in the UV-Vis spectra, with a hypsochromic shift observed in all cases, more pronounced in azo-based fluorinated chains due to H-bonding on the nitrogen sites, chain conformations, and planarity. The optical band gap (Eg) of the polymers was determined from the UV-Vis data, with the Eg values of the azo-based fluorinated polymers being 1 eV higher than those of their corresponding linkers. The cross-linking formation was characterized by porosity measurements, with the azo-based polymer exhibiting the highest surface area of 770 m²/g with a pore volume of 0.35 cm³/g, while the open-chain azomethine-based polymer exhibited the lowest surface area of 285 m²/g with a pore volume of 0.0872 cm³/g. Porous structures with varied hydrophobicities were investigated as adsorbents for separating water-benzene and water-phenol mixtures and selectively binding methane/carbon dioxide gases from the air. The most hydrophobic polymers containing the decafluorbiphenyl linker were suitable for benzene separation, while the best methane uptake values were 6.14 and 3.46 mg/g for DAB-Z-1O and DAB-A-1O, respectively. DAB-Z-1h, with the highest surface area and rich in nitrogen sites, exhibited the highest CO₂ uptake at 298 K (17.25).

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. 3D pharmacophore models are powerful computational tools in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning will be highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

Transition to renewable energy has been recognized as a crucial step to addressing climate change and achieving greenhouse gas reduction targets, but it can also cause energy sprawl if not planned properly. Clean renewable energy communities (CREC) are emerging globally as an approach for decentralized energy systems and an alternative to traditional centralized energy systems. CREC aims to lower the energy carbon footprint, enhance local energy resilience, and improve the quality of life of residents. Through a comprehensive literature review, this study reviews metrics that can assess the impact of energy transition plans and support decision-making to select technologies that create efficient, reliable, and accessible energy systems. It classifies these metrics into a five-dimensional sustainability approach: environmental, technical, social, economic, political and institutional. The paper proposes a conceptual framework to guide decision-makers in recognizing the role of sustainable land development, sustainable energy planning, and resiliency as an integrated approach to energy transition planning. This framework stresses mapping the place-based potential for clean renewable energy at various scales, highlights the importance of resilience in energy planning, and addresses challenges associated with energy source selection, built environment efficiency, and energy trade. While the framework can serve as a starting point for evaluating energy transition plans, further work is needed to address the limitations of existing metrics and identify additional evaluations for energy mixed land use that are critical to managing energy sprawl on ecosystem services and other land uses.

Building energy demand impacts a myriad of interconnected economic, societal, and environmental aspects. As a result, Buildings Energy Models (BEM) play an important role in the process of urban design and planning. While previous studies have investigated the effects of building interventions on energy efficiency, their applicability may be limited due to the BEM’s high computational complexity. This limits their ability to systematically study important aspects of energy demand on a large scale. The development of Machine Learning Models (MLM) allows to design the required detailed analysis and solutions, while reducing the computational burden, making MLM attractive for urban designers. The capability of MLM to generalize well for multiple contexts (in our case, multiple buildings) is a crucial contributor to their applicability. However, the validation process in a wider context is often overlooked, therefore its generalization capabilities are not quantified. In this paper, we present a framework to train and validate a surrogate model derived from a physics-based BEM. Our method employs a Multiple Linear Regression model to predict Energy Use Intensity (EUI) for office buildings in Singapore using 36 input parameters (covariates), based on a training dataset of 23,000 samples. Model validation is performed by comparing the results of the Surrogate Model (SM) to a widely used BEM for a sample of 120 buildings. Our results indicate that the SM has an accuracy of NRMSE of 13%, NMBE of −3.56%, and R2 of 0.92, which suggests it can effectively and accurately predict building EUI. We also conduct a sensitivity analysis, which indicates that the parameters associated with internal loads and internal space usage are the most influential. Additionally, we present a reduced order model trained with only the 11 most influential parameters, which exhibits negligible loss in accuracy compared to the full SM while providing reduced complexity. Finally, we demonstrate an application of our SM to evaluate energy efficiency under uncertainty scenarios. The analytically derived results indicate a potential reduction of EUI of offices in Singapore from 227kWh/m2 to 99kWh/m2 by altering the building parameters that were identified as most influential.

Energy transition is one of the greatest challenges facing humanity today. All micro, small, medium and large enterprises in operation will have to face it, and some can al-ready boast the first successes in this regard. This paper focuses on the involvement of these smallest players in energy transition. Micro and small entities are the largest part of the economy, having a great influence on its development. Looking at the wide scale of their activities, these entities are distinguished by a specific owner-manager relationship. This means that the final decision-making is influenced by business goals, but also by the manager's approach, their awareness of changes, and personal and other factors. Based on a structured and standardized survey questionnaire, the research was carried out using the Computer-Assisted Telephone Interviewing technique between April and May 2022 among owners of micro and small enterprises operating in Poland. The survey showed that active engagement of micro and small enterprises in energy efficiency initiatives is related to their market success and financial health.

This study explores the advanced integration of Bi2Te3-based thermoelectric systems in Ocean Thermal Energy Conversion (OTEC) to optimize performance. OTEC, a promising renewable energy source, needs help in efficiency maximization. We address this by implementing Bi2Te3 thermoelectric generators (TEGs), known for their high thermoelectric efficiency at low-temperature differences. Our research encompasses a comprehensive simulation and analysis of these TEGs in OTEC systems, examining material properties, system design, and environmental impact. We present a novel approach to integrating these TEGs, optimizing their arrangement, and interfacing with the OTEC cycle to enhance overall efficiency. The study also investigates the durability and cost-effectiveness of Bi2Te3 TEGs in marine environments. Results demonstrate a significant improvement in energy conversion efficiency, highlighting the potential of Bi2Te3 TEGs in sustainable ocean energy exploitation. This work advances material science in renewable energy, providing valuable insights into the practical application of thermoelectric materials in large-scale energy systems. It paves the way for future research on material optimization and system integration, essential for developing efficient and sustainable OTEC systems.

Sport centers are significant energy consumers. This article outlines the engineering design for a comprehensive energy performance upgrade of the indoor sport hall in Arkalochori, Greece, and presents the projected results. The indoor sport hall constitutes a major sport facility in the mainland of Crete, hosting a broad cluster of sport municipal activities and the official basketball games of the local team in the 2nd national category. Having being constructed in the mid ‘90s, the facility exhibits very low thermal performance, with considerably high U-factors for all constructive elements (from 4 to 5 W/m2∙K), still use of diesel oil for indoor space heating and domestic heat water production and ineffective, old lamps and luminaries covering the lighting needs of the facility. The energy performance upgrade of the indoor sport hall was holistically approached, through all possibly applicable passive and active measures: insulation of opaque surfaces and replacement of openings, installation of new stone wool panels, installation of heat pumps for indoor space conditioning, elimination of diesel oil for any final energy use, installation of a solar-combi system for domestic hot water production, upgrade of all indoor and outdoor lighting equipment, installation of solar tubes on the main sport hall roof for natural lighting and installation of a photovoltaic plant on the same roof for the compensation of the remaining electricity consumption. With the proposed measures, the indoor sport hall is upgraded to zero energy facility. The payback period of the investment was calculated at 26 years, on basis of the avoided energy resources procurement cost. This work has been funded by the Horizon 2020 project with the acronym “NESOI” and was awarded with the public award of the “Islands Gamechanger” competition of the NESOI project and the Clean Energy for EU Islands initiative.

This paper results from our investigation into novel means of electromagnetic propulsion. It requires the basis of our claims to be put on a sound theoretical footing regarding the purported momentum exchange with the electromagnetic field. One of these concerns is the huge discrepancy between the energy density of the Zeropoint and its purported manifestation as the Cosmological Constant. Here we state that it is manifestly wrong to introduce the zeropoint at zero order into the stress-energy tensor, because it is something which describes zero particle count. As a fluctuation, it belongs in a higher order Taylor expansion in frequency of the stress-energy tensor. Furthermore in the 3^rd order in the Einstein constant our procedure is some 9 orders of magnitude too small. We make up this difference by suggesting that vacuum energy is much higher still and that more degrees of freedom exist in physics beyond the Standard Model or that there is interaction energy between the modes.

According to the current understanding, the recently observed accelerated expansion of the universe is caused by the dark or the vacuum energy. Attempts to calculate the magnitude of this energy using the standard model of particle physics led to values which are 59 – 120 orders of magnitude larger than the experimentally estimated one. Even though the expanding space has positive internal energy, in a flat universe it is completely balanced by the negative energy of gravitational field making the net energy equal to zero. However, the current physical theories may breakdown for times less than or on the order of Planck time and one cannot assume that the above assertion concerning the balance of two energies is valid also in this time scale. In this note it is assumed that this balance of the two energies during the creation of new space as the universe expands takes place only for times larger than the Planck time. If this assumption is correct, the net energy of the newly created space remains positive for times on the order of Planck time and the positive vacuum energy has to be burrowed from empty space before it is being balanced by gravity. This can happen only within the restrictions of the time-energy uncertainty principle. In this note it is shown that such considerations lead to a vacuum energy density of about 0.3 Nanojoules per cubic meter which has to be compared with the measured value of 0.6 Nanojoules per cubic meter.

At 100% penetration, intermittent renewable energy causes outages. To flatten the leaping supply and demand curves of renewable energy, an energy storage medium and carrier are required. The Hydrogen energy stotrage system (HydESS), which combines a proton exchange membrane (PEM)-based water electrolysis (WE) system and a fuel cell (FC), has the quickest response time, the longest duration, and the greatest storage capacity. HOMER tools for techno-economic analysis are used to evaluate the HydESS hybrid system. Kyiv, Ukraine, is appealing due to its spatial-temporal wind speed, solar irradiance, and energy security. With an LCOE of $0.245 kWh−1 and 9.3% fuel cell power output, the most feasible solution is onshore wind turbine-coupled PEMFC HydESS. As a result 1,000-kW wind power, 250-kW PEMFC, 250-kW PEMWE, and 441-kW BESS are optimized to serve a 3,900 kWh day academic residence load. When compared to a diesel generator and a BESS, the hybrid HydESS reduces CO2 emissions by 4,132 tons over a 25-year period. The LCOE of a hybrid HydESS system is determined by the SOFC lifetime rather than the PEMFC's 2% difference between 40,000 and 50,000 h with the same rated power. This study recommends accelerating PEMFC development with fluctuation-endurable materials and design.

Unmanned aerial vehicles serving as air base stations (UAV-BSs) are used in telecommunications, when the network is overloaded or unavailable, providing access to users on the ground. Due to its dynamics of providing access to networks in different altitudes, the electromagnetic signal of UAVs suffers losses along the path, thus facing several problems related to transmission, such as: attenuation, fading and distortion. In this work is proposed a 3D positioning strategy for UAVs, taking into account height, coverage radius and flight energy efficiency. Results show that the proposed algorithm is able to position the UAVs in suboptimal locations, providing network access for mobile users, maximizing the number of connected devices in the UAV-BS and saving energy, thus obtaining better performance than the approaches conventional.

Although the gravitational force is well known for its extremely weak value in the framework of ordinary macroscopic objects at our scale, the generally discredited framework of pushing gravity might lead us to discover a huge source of energy which is mostly lost during the action of the gravity force due to the large distances between nucleons compared with their diameters. In case this energy is identified and partially diverted from its role in gravity, we would benefit from a gigantic reservoir of energy reminiscent of Nikola Tesla's dream of a free unlimited energy source

The generation at the helm faces an unprecedented responsibility in the near future of artificial intelligence. The implications of setting up the founding rules that will regulate the operation of AI are heavy since after they’re set they last forever. Once this first AI is commenced, it can be such that no other subsequent AIs could emerge thereby assuming dominion over its own creation stand. As a result, retaining control becomes necessary. Lest humanity surrender agency to its own creation. At this juncture of big talks, critical issue are raised concerning AI administration owners. Is it appropriate for only a few people to have unrestricted control on AI commands while leaving out all precautionary measure? Therefore, we have to always consider between control and constraint when dealing with AI issues which involves authority plays off against morality. The direction Artificial Intelligence takes in the future depends on the decisions made by today’s generation. We will determine how we are viewed historically in terms of technology based on how well we take on such an important duty. There’s a major turning point ahead of us where we who are the stewards of tomorrow must make a choice that protects humanity’s right to self-determination and also exploits the power of AI for change.

Coronary heart disease (CHD) has become a prevalent issue in Indonesia, resulting in a majority of fatalities and illnesses. The most recent guidelines in the United States for managing patients with chronic coronary syndrome (CCS) have undergone significant changes, with the recommendation for conducting percutaneous coronary intervention (PCI) strictly limited to patients who continue to experience symptoms despite receiving optimal medical treatment (OMT). However, adopting this approach may lead to increased morbidity and mortality due to the presence of vulnerable plaque. Vulnerable plaque is characterized by minimal obstruction during coronary angiography (40-70%) and may not produce ischemia during physiological studies. Identifying vulnerable plaques can be accomplished through several methods, among which computed tomography coronary angiography (CTCA) is the most cost-effective approach. Recently, experts have employed different techniques, either with stents or drug-coated balloons, to manage vulnerable plaques, showing the methods to be safe and effective. The other challenge faced by patients with CCS is the possibility of achieving complete revascularization (CR) to reduce the likelihood of future major adverse cardiac events (MACE) and mortality. Both approaches, identifying and intervening in the vulnerable plaque, are difficult to perform in developing countries due to the limited budget. Studies have shown that plant-based diets (PBD) can reverse atherosclerotic plaque and stabilize vulnerable plaque, possibly leading to CR. PBD is an affordable and effective approach, but it requires healthcare providers skilled in educating and implementing this dietary approach with their patients. Large-scale randomized controlled trials in PBD, specifically aimed at addressing the vulnerable plaque and achieving CR, are necessary before implementing the PBD intervention.

A significant portion of fatalities stemming from acute myocardial infarction (AMI) occur suddenly and without warning. This can be prevented through early detection of coronary stenosis. In recent years, coronary artery calcium (CAC) and computed tomography coronary angiography (CTCA) have been utilized. For instance, a 55-year-old interventional cardiologist had a CAC of 58 with 50% stenosis involving his left anterior descending (LAD) artery. Despite possibly having vulnerable plaque, which carries significant future cardiovascular events, the patient followed a plant-based diet (PBD), took supplements, and continued optimal medical therapy (OMT). After three years, there was a significant regression in both CAC and stenosis. During our discussion, we emphasized the importance of food selection, quantification, and how food is processed (raw) to achieve maximal benefit in regressing atherosclerosis. Additionally, we highlighted the importance of identifying the possibility of vulnerable plaque and the mechanism stabilizing it. The case supported the guidelines set by the American College of Cardiology (ACC) and the American Heart Association (AHA) for managing chronic coronary syndrome (CCS) by emphasizing OMT and lifestyle changes. We hope that in the future, more doctors and patients will take responsibility for implementing lifestyle changes rather than solely relying on medications and sophisticated medical technologies.

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

As belonging to one of the most isolated continents on our planet, the microbial composition of different environments in Antarctica could hold a plethora of undiscovered species with potential for biotechnological applications. This manuscript delineates our discoveries after an expedition to the Bulgarian Antarctic Base "St. Kliment Ohridski" situated on Livingston Island, Antarctica. Amplicon-based metagenomics targeting the 16S rRNA genes and ITS2 region were employed to assess the metagenomes of the bacterial, fungal and archaeal communities across diverse sites within and proximal to the research station. The predominant bacterial assemblages identified included Oxyphotobacteria, Bacteroidia, Gammaprotobacteria and Alphaprotobacteria. A substantial proportion of cyanobacteria reads were attributed to a singular uncultured taxon within the family Leptolyngbyaceae. The bacterial profile of a lagoon near the base exhibited indications of penguin activity, characterized by a higher abundance of Clostridia, similar to lithotelm samples from Hannah Pt. Although most fungal reads in the samples could not be identified at the species level, noteworthy genera, namely Betamyces and Tetracladium, were identified. Archaeal abundance was negligible, with prevalent groups including Woesearchaeales, Nitrosarchaeum, Candidatus Nitrosopumilus and Marine Group II.

Abstract: Background: Dental fear and uncooperative behavior can hinder dental treatment quality. Pediatric Procedural Sedation and Analgesia (PPSA) is used to facilitate treatment when coping capacity is exceeded. Out-of-hospital PPSA has been associated with more adverse outcomes compared to hospital-based settings. Updated Dutch PPSA guidelines recommending two anesthesiologists and a ventilator during out-of-hospital PPSA have increased costs and raised concerns about the accessibility to specialized high quality dental care for children in the Netherlands. Aim: This study aims to investigate the impact of the updated 2017 guideline on the occurrence rate of adverse events during PPSA, as recorded in the anesthesia complication database of twelve participating Dutch dental clinics, with adjustments made for age, weight, and duration of treatment. Method: Data of 25,872 children treated at 12 dental clinics performing out-of-hospital PPSA between 1997 and 2019 were analyzed. Eligible children were aged 2-18 with ASA classification I and II. A logistic two-level mixed-effects model is used to estimate the updated guideline’s impact on adverse events. Results: In the final model we analyzed the data of 21,759 children in nine dental clinics. The OR of the occurrence rate of an adverse event adjusted for age, weight, and duration of treatment was 0.75 (95% CI 0.64 – 0.89) after the implementation of the updated guideline, compared to before the updated guideline. This outcome was significant with a P=0.001, indicating a protective effect. The average odds per clinic of having an adverse event was 0.04 with a 3.81% chance. Conclusion: Our findings demonstrate a significant reduction in adverse events after the implementation of the updated guideline and highlight the importance of adherence to evidence-based guidelines in improving patient safety and the quality of care provided during pediatric procedural sedation and analgesia in out-of-hospital dental clinics. Our study underlines the necessity of considering the complex underlying mechanisms that play a role in the development and implementation of evidence-based practice and safety initiatives on the occurrence of adverse events.

The healing of osteochondral defects (OCD) resulting from injury, osteochondritis, or osteoarthritis in middle- and old-age individuals, bearing lesions in the cartilage and bone, pain, and loss of joint function, presents challenges to the clinical practitioners because of non-regenerative cartilage and the limitations of current therapies. Bioactive peptide-based osteochondral (OC) tissue regeneration is becoming more popular because it does not have the immunogenicity, misfolding, or denaturation problems associated with original proteins. Periodically, reviews are published on the regeneration of bone and cartilage; however, none of them address the simultaneous healing of these tissues in the complicated heterogeneous environment of the OC interface. As regulators of cell adhesion, proliferation, differentiation, angiogenesis, immunomodulation, and antibacterial activity, potential therapeutic strategies for OC defects utilizing bone and cartilage-specific peptides should be examined and investigated. Therefore, the main goal of this review is to study how they contribute to the healing of OC defects, either alone or in conjunction with other peptides and biomaterials.

In the field of modeling social systems, the use of agent-based models (AM) has become a powerful tool for understanding processes occurring in society, in particular, for studying the spread of infections among the population. The agent model is based on a synthetic population consisting of agents, their interactions and sometimes the territory in which the interaction occurs. The choice of an appropriate method for creating such artificial populations and the selection of parameters becomes a key issue affecting the realism of the model and its ability to reflect real-world scenarios. This article delves into the interplay between agent-based modeling, synthetic population construction, and parameter selection, offering our insight into the complex process of modeling infectious disease dynamics. The review concludes with an exploration of extending the Covasim model [1] to experiments in computational epidemiology. Covasim, a dynamic transmission modeling tool, is known for its ability to adapt to different scenarios and offers predictive information critical for managing epidemiological situations. The Covasim model could include improvements to better simulate real-world epidemic conditions, including a revised transmission strategy to account for accumulation through daily contacts. In addition, we propose to add a module for intercity travel and a visualization package that will significantly expand the functionality of Covasim. These improvements make Covasim a more versatile and efficient tool for modeling and analyzing epidemic processes.

AI-based approaches are increasingly being used to revolutionize vaccine development for COVID-19. Small molecules, peptides, and epitopes are being collected for therapy discovery, which could also direct AI-based models, screening, or generation [1]. AI-based models are being used for drug discovery and vaccine development, and pre-existing data is being leveraged through machine learning approaches for COVID-19 drug discovery and vaccine development [1]. The best candidate targets for future treatment development are being identified and evaluated using AI-based models [1]. AI-based approaches can be used to overcome challenges in manufacturing, storage, logistics, and safety and efficacy issues related to different vaccine candidates for COVID-19 [2]. AI algorithms can help identify the best vaccine candidates for COVID-19 while considering the efficiency of antigenic peptides for immune response generation [2]. The presentation of antigenic peptides by major histocompatibility complex (MHC) class II molecules is the first step after COVID-19 vaccine administration for any vaccine-induced immune response [2]. Thus, AI-based models are being used to identify the best vaccine candidates for COVID-19 and to ensure that the vaccine-induced immune response is efficient and safe. The utilization of AI-based methods to address logistical, manufacturing, storage, safety, and efficacy issues regarding several COVID-19 vaccine candidates will be examined in this study. Additionally, while considering the effectiveness of antigenic peptides for the induction of an immune response, we will determine the best potential targets for the next treatment development and assess how AI-based models can help discover the best vaccine candidates for COVID-19. This research ultimately intends to offer insights into how AI-based techniques can transform COVID-19 vaccine development and how they can be applied to address vaccine development issues. In this paper, we focus on recent advances in using artificial intelligence to develop COVID-19 drugs and vaccines, as well as the potential of intelligent training in discovering COVID-19 therapeutics, highlighting potential challenges and solutions.

The coronavirus disease (COVID-19) is a respiratory tract infection caused by the new virus SARS-CoV-2. In March 2020, the World Health Organization (WHO) declared a worldwide COVID-19 pandemic. Since then, there has been a rush to find preventative or curative treatment. This arrived with the appearance of the COVID-19 vaccine, months after the declaration of the pandemic. In the setting of global COVID-19 immunization, there have been reports of rare skin immune-mediated diseases (IMD) after COVID-19 vaccination, particularly systemic sclerosis. Systemic sclerosis (SSc) is the most reported new onset IMD following messenger RNA (mRNA) COVID-19 vaccination. The pathogenesis of SSc is not fully understood, and the diagnosis is based on clinical symptoms. We report a 54-year-old male, with no prior history of autoimmune disorder, with rapid and progressive skin thickening (Modified Rodnan skin score 30/51), weeks after his mRNA COVID-19 vaccination. Antinuclear antibodies (ANA) showed a strongly positive nuclear fine-speckled pattern, but no SSc autoantibodies were found. The patient was diagnosed with SSc based on the persistence of autoantibodies and the clinical criteria according to the 2013 American College of Rheumatology/European League Against Rheumatism classification. Due to the proximity of the two events, we hypothesized a cross-reaction between COVID-19 vaccination and, in genetically predisposed patients, an emergence of systemic sclerosis. Our case suggests a potential relationship between the mRNA COVID-19 vaccine and new-onset autoimmune diseases. Physicians should be aware of this possible association.

The corrosion resistance of two cast CoCr based alloys, with different amounts of chromium and with different alloying elements in the bulk composition of the alloy, was assessed. Herewith, we investigated the corrosion behavior of Co21Cr8Mo7W and Co29Cr7W by open circuit potential (OCP), potentiodynamic polarization (PP) and electrochemical impedance spectroscopy (EIS) in 0.1 M phosphate buffer solution (PBS) at 37°C for long time immersion. After 1000 hours of immersion, the corrosion current density (icor), estimated from anodic polarization tests, is lower for Co21Cr8Mo7W (i.e. 49 nA cm-2) alloy compared to Co29Cr7W alloy (180 nA cm-2). As regards the corrosion potential (Ecor), a nobler value was observed for Co21Cr8Mo7W (i.e. -59 mV vs. Ag/AgCl) compared to Co29Cr7W (i.e. -114 mV vs. Ag/AgCl). These findings suggest a better corrosion resistance of the film formed on the alloy containing lower amount of Cr and two alloying elements, namely Mo and W. These results are promising ones in terms of medical applications because they open new strategies in obtaining alloys with lower content of chromium and with higher protective properties against corrosion attack.

The global construction sector contributes a significant share of total greenhouse gas (GHG) emissions. In Australia, infrastructure activity alone generates 18% of the GHG emissions budget. The use of low-embodied carbon building materials is crucial to achieving sustainability in the construction sector and to fulfill national and international climate goals. Industrial hemp (Cannabis sativa L.) is considered a promising feedstock for sustainable construction materials because of its biogenic carbon content, fast-growing cycles with low agricultural input requirements, and technical functionality which is comparable to traditional materials. This study has applied the life cycle assessment (LCA) guideline of ISO 14040:2006 to estimate the carbon footprint (CF) expressed in carbon dioxide equivalent (CO2eq) emissions of hemp-based building materials in Western Australia. The functional unit is 1 m2 of hemp-based board, and the system boundary includes cradle-to-gate stages, i.e., pre-farm, on-farm, and post-farm activities. The production of 1 m2 of hemp-based board is estimated to be - 2.302 kgCO2eq. Electricity from the public grid for lignin extraction during the post-farm stage is the main contributor to total CO2eq emissions (26%), followed by urea production (14%) during the pre-farm stage. Overall, the use of electricity from the SWIS during the post-farm stage accounts for 45% of total emissions. Sensitivity analysis shows that the CF of hemp-based boards is highly sensitive to the source of energy, i.e., total replacement of the SWIS by solar power decreases the CF from - 2.30 to -6.07 kgCO2eq (164%). The results suggest that hemp-based boards exhibit lower embodied GHG emissions compared to traditional materials, such as gypsum plasterboards.

TiC particles were prepared from Ti and C powders, and TiC-reinforced Cu-based composites were prepared by powder metallurgy with SPS sintering. The effects of SPS sintering temperature and TiC particle reinforcement phase content on the electrical and mechanical properties of Cu-based composites were investigated. The reinforcement of TiC particles by thermal mismatching at increasing sintering temperature has a significant strengthening effect on the composites, but the high temperature causes softening of the Cu matrix and the growth of grains reduces the properties of the composites. In this study, the effects of porosity and density on the composite properties were not significant.

Background. We created agent-based model for short- and longterm forecasting of COVID-19 and for evaluation how the actions of the regulator affected the human and material resources of the healthcare system. Methods. The model was implemented in the AnyLogic software. It includes two state charts – social network and disease transmission. The COVID-19 Essential Supplies Forecasting Tool (COVID-ESFT, version 2.0) was used to determine healthcare resources needed. Results. Satisfactory results were obtained with long-term (up to 50 days) forecasting in the case of a monotonous change in total cases curve. However, if periods of relative stability are accompanied by sudden outbreaks, relatively satisfactory results were obtained with short-term forecasting, up to 10 days. Simulation of various scenarios showed that the most important place for the spread of infection are families. Wherein the maximum number of cases of COVID-19 is observed in the age group of 26-59 years. Due to a set of measures taken by government agencies, the number of cases in Karaganda city was 3.2 times less than was predicted in “no intervention” scenario. Economic effect is estimated at 40 %. Conclusion. Performed model is an attempt to consider as much as possible the peculiarities of the socio-demographic situation in the country. In the future, we will be prepared to some extent for challenges like those we have experienced in the past three years.

Progress in macrophage research is crucial for numerous applications in medicine, including cancer and infectious diseases. However, the existing methods to manipulate living macrophages are labor intense and inconvenient. Here we show that macrophage membranes can be reconstituted after storage for months at 4C, with their CD206 receptor selectivity and specificity being similar to that in the living cells. Then, we have developed a mannose ligand, specific to CD206, linked with PEG as IR spectroscopy marker to detect binding with the macrophage receptor. PEG was selected due to its unique adsorption band of C-O-C group at IR spectra, which does not overlap with other biomolecule’s spectroscopic feature. Next, competitive binding assay versus the PEG-bound ligand, has enabled selection of other higher-affinity ligands specific to CD206. Further, those higher-affinity ligands were used to differentiate activated macrophages in patient’s bronchoalveolar (BAL) or nasopharyngeal (NPL) lavage. CD206- control cells (HEK293T) showed only non-specific binding. Therefore, biochips based on reconstituted macrophage membranes as well as PEG-trimannoside as an IR spectroscopic marker, can be used to develop new methods facilitating macrophage research and macrophage-focused drug discovery.

Background: The incidence of prostate cancer (PC) has been risen annually. Despite the diagnosis is made mainly with non-metastatic PC, mortality is explained by the metastatic disease (mPC). Without a doubt there is an intermediate scenario in which patients have no mPC but will have initiated a metastatic cascade through an epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict what patients will progress in the future to non-localized clinical disease or already have micrometastatic disease, and therefore, will clinically progress after primary treatment. Biomarkers for predicting mPC are still under development; there are few studies and not much evidence of their usefulness. Summary: This review is focused on tissue-based genomic biomarkers (TBGB) for predicting metastatic disease. We developed four main research questions that will attempt to answer according to the current evidence. Why is important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are current prostate cancer treatments? Key Messages: Knowing useful predict tools could help determine which patients may need multimodal or adjuvant treatment even with a localized disease, and in consequence, what patients do not need more than a single modality of treatment. The importance of predicting metastasis is fundamental, given that once metastasis is diagnosed, the quality of life (QoL) and survival drop dramatically.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

The paper explores the potential of a low-cost advanced video-based technique for the assessment of structural damage induced to buildings by seismic loading. A low-cost high-speed video camera was utilized for motion magnification (MM) processing of footages of a two-story reinforced concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e. in terms of modal parameters) and the structural deformations of the building in the MM videos. The results by MM were compared for method validation to damage assessment obtained by the analyses of conventional accelerometers and high-precision optical markers tracked by a passive 3D motion capture system. Also, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometers were also processed and analyzed by using several stationary and non-stationary techniques with the aim to analyze the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed MM-based procedure provided accurate estimate of the main modal frequency and the damage location through the analysis of modal shapes, which were confirmed by advanced analyses of accelerometric data.

The deployment of Electro-Mechanical Actuators plays an important role towards the adoption of the More Electric Aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs in place of more traditional hydraulic solutions is still set back due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA Prognostics and Health Management (PHM) system, capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The authors have developed a model-based prognostic framework for PMSM based EMAs leveraging a metaheuristic algorithm: Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity and proportional gain) are simulated thanks to a Reference Model, acting as a Numerical Test Bench, then detected and identified thanks to the envisioned prognostic method, which leverages a low fidelity Monitoring Model. The employed algorithms showed good results and prove that this strategy could be executed in pre-flight checks or during the flight at specific time intervals, with positive impacts on system safety and availability.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library. The version of SamPy considered in this paper is available at https://github.com/sampy-project/sampy-paper .

Good estimators are characterized as robust, unbiased, efficient, and consistent. However, the commonly used estimators are weak or lack one or more of these properties. In this article, eight (8) estimators for statistical and geometrical estimations of symmetry/asymmetry, similarity/dissimilarity, identity/unidentity, and feature transformation were proposed following Kabirian-based optinalysis and other operations. The proposed estimators are characterized as invariant (robust) under scaling, location shift, and rotation or reflection. A computing code was written in python language for each of the proposed estimators so that peers can have working codes for application and performance evaluation.

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

There is increasing interest in automating train operations of mainline services, e.g. to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but not implemented on a large scale. Before the general introduction of new or adapted technologies can have a transformative effect on the operation of such a complex system as train operation on mainlines, they have to pass functional, interoperability and performance tests. A virtual preliminary analysis is one way to ensure a smooth as well as safe introduction and implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. To demonstrate the developed approach, a straightforward investigation of a case study is conducted using a microscopic train simulator in combination with an ATO algorithm.

Background: In an age where information is generally accessible, most of the interest these days has focused on how accessible and convenient technology can be. So small and personal, mobile devices can transform our perception of learning by combining both mobility and convenience. Mobile learning is part of the digital learning landscape alongside e-learning and serious games. However, knowledge about effective design of mobile learning experiences remains of interest with a focus on appropriate design models and the embodiments that can be implemented to achieve the intended educational outcomes. Exploring the instructor's perspective on mobile learning is essential. Therefore, the aim of this study was to investigate the Moroccan instructors' perception and practice of mobile learning to inform the development of an ecologically valid mobile learning integration model. Methods: Higher education Instructors (n=41) were recruited to the study. The Moroccan instructors' perception and their experiences regarding their adoption of mobile learning were collected using an online survey. The analysis focused on their mobile use, perceived IT competency, and opinions on mobile learning. Results: We described most of the instructors' considerations regarding integrating mobile technologies into their teaching activities. We found that most of the mobile learning activities defined by the respondents corresponded to relatively advanced use of mobile devices. More promising, instructors have found innovative ways to use the educational potential of mobile devices. However, the prospect of mobile devices was still to challenge. No or poor Wi-Fi connection, number of devices or limited access, sometimes fees or applications incompatibility were identified as reasons and obstacles to mobile learning usage. Conclusion: Mobile learning is mostly perceived positively among Moroccan instructors allowing many applications and usage to enhance teaching and learning. In this study, a better understanding of aspects and factors influencing the integration of mobile learning in the Moroccan educational context is exposed, helping further the development of an ecologically valid mobile learning integration model. Future work on mobile learning should consider the highly paced evolution of mobile technologies, emphasizing the flexibility of integration frameworks to support instructors and learners.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.

This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

Has anyone ever missed an event because he was confused in days and dates? Do we remember the date of any day without looking at a calendar? Is the current Gregorian Calendar efficient enough for use, and does it facilitate our life or make it more complicated? Have you ever thought about a much simpler way to calculate days and dates in a year? All these questions are answered in this paper, in which the author proposes original optimization algorithm that creates optimal perennial calendars. Results show that there is more than one way to create a perennial calendar, in which the number of days in each month does not change, neither the dates. Hence, all months have the same sequence of days and dates. In other meaning, Monday becomes the first day of every month, and Sunday becomes the last day. Consequently, the calendars become much easier to memorize and very simple to predict the days and dates in any year.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 0.8x1e6 Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer or simulant Urine) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 1.2x10⁶ Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10^-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5-min and 1-km) radar reflectivity data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used to evaluate the proposed algorithm. The performance of the new algorithm is compared with a radar reflectivity map and verified by two object-oriented methods: structure–amplitude–location (SAL) and geometric index. The verification results suggest that the performance of the new algorithm is good. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

The quadruple burden of diseases, early discharge from hospital and hospital at home have resulted in home-based care services becoming a requirement in South Africa. The home-based care services generate a significant amount of health care risk waste that is mismanaged. However, more attention is given to the health care risk waste generated in hospitals and clinics than to health care risk waste generated by home-based caregivers. Therefore, this study investigates the health care risk waste management practices by home-based caregivers. The study adopted a mixed research approach, qualitative and quantitative methods, using a literature review, interviews, and questionnaires as means of data collection. Results show that there are different types of health care risk waste generated as a result of different activities performed by home-based caregivers, but that the waste was found to be managed in an unsafe manner. The majority of households receiving home-based care did not have basic sanitation facilities such as toilets, running water and waste removal services, aggravating the issue of health care risk waste mismanagement. The study recommends a new policy framework that will lead to safe management practices of generated health care risk waste to be adopted by home-based caregivers.

The application of graph theory in chemical and molecular structure research far exceeds people's expectations, and it has recently grown exponentially. In the molecular graph, atoms are represented by vertices and bonded by edges. Closed forms of multiplicative degree-based topological indices which are numerical parameters of the structure and determine physico-chemical properties of the concerned molecular compound. In this article, we compute and analyze many multiplicative degree-based topological indices of silicon-carbon Si₂C₃-I[p,q] and Si₂C₃-II[p,q].

Recent discovery of triangular boron Nanotubes makes it a competitor of carbon in many respects. Closed forms of M-polynomial of nanotubes produce closed forms of many degree-based topological indices which are numerical parameters of the structure and, in combination, determine properties of the concerned nanotubes. In this report, we give M-polynomials of triangular boron nanotubes and recover many important topological degree-based indices of these nanotubes. We also plot surfaces associated to these nanotubes.

To enable net zero sustainable thermal building energy this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HP) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using an System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.

Low energy consumption is a significant contribution of new and retrofitted buildings that place an emphasis on energy efficiency. Although the mandatory requirements of the U-factor and R-value are described in the Vietnamese National Technical Regulation on the energy efficiency of buildings—QCVN09:2017/BXD, these values are not widely publicized, leading to designers facing difficulties when choosing suitable materials at the design stage. This study focuses on the method used to declare the heat transfer values of building materials based on the energy labeling process in order to create an energy labeling program for building materials. To accomplish this objective, criteria and labeling procedures for windows, doors, walls, and building roofs have been developed. In addition, a case study of insulation product labeling was used to evaluate the applicability of the developed labeling program in the implementation phase.

Purpose of publication: - Preparation of bibliometric data exported from the Scilit platform on energy efficiency and conservation for further analysis to identify relevant research topics. - To identify potential issues in the processing of data exported from the Scilit platform. - Providing colleagues with the opportunity to use the prepared data and examples of their analysis for independent research on topical issues of energy efficiency and energy conservation using materials provided by the Scilit platform. Research Materials: Files in CSV and RIS formats exported from Scilit for the query "energy conservation OR efficiency" in Common Fields [Title, Abstract, Keyword], using filters: Content Type → Journal Article; Year→2021-2023; Subject → Industrial Engineering (29.8K), Energy and Fuel Technology (9.8K), Manufacturing Engineering (9.2K). A total of 30K records sorted by their relevance (10K for each year) were exported. Data are current as of 14-03-2024. Methods: Preprocessing of title, annotation, and keyword field texts using lemmatization dictionaries collected on GitHub, removal of keywords taken from GATE and spaCy, and "manual" editing. Using VOSviewer to analyze publication topics by clustering keywords based on their co-occurrence. Using Scimago Graphica to build bubble diagrams.Application of the GSDMM algorithm for clustering bibliometric records by title and annotation texts. Creation of a dictionary for this algorithm using the keyword field.Use of the Carrot2 demo version and the NMF algorithm for a more detailed analysis of the topics of the record clusters obtained from GSDMM. Results: are presented in the form of initial and interim tables and graphs obtained in the course of this study. The full tables are provided as references to the attached materials. Supplementary material for this preprint on figshare: Chigarev, Boris (2024). Supplementary material for preprint "Analyzing the Possibilities of Using the Scilit Platform to Identify Current Energy Efficiency and Conservation Issues". figshare. Dataset. https://doi.org/10.6084/m9.figshare.25574058.v1

Wind turbines are a solution for sustainable energy, significantly reducing carbon emissions and fostering a circular economy for more cost-effective and cleaner power generation, in line with worldwide environmental aspirations. In this context, this research aims to explore a novel Two-Stage Twisted Blade micro-Vertical Axis Wind Turbine ($\mu$-VAWT) alternative inspired by the Savonius Rotor (SR). This investigation utilizes the $\kappa-\omega$ SST turbulence model to explore the power coefficient ($C_P$) and torque coefficient ($C_T$), finding $C_P$ values ranging from 0.02 to 0.04 across the turbine by altering the free stream velocity ($V$). $C_T$ analysis further delves into four specific sections, highlighting areas of particular interest. These results are validated by examining velocity contours, pressure contours, and streamlines in four horizontal sections, demonstrating that the proposed turbine model exhibits minimal torque fluctuation. Moreover, the analysis of vertical wind streamlines illustrates very low interference with various wind turbine proposals, underscoring the turbine's efficiency and potential for integration into diverse wind energy projects.

This paper focuses on the paradigm that explains the link between energy transitions and financial transformations. In particular, it attempts to shed some light on the following question: How did the transition from wood to coal affect financial development in late 18^th and early 19^th century Britain? The results demonstrate that the energy transition in Britain during the 1^st Industrial Revolution constituted an important moment in the country's financial development and can be used as an example of how an energy innovation can foster the financial development of particular countries or regions.

Different severe energy crises episodes occurred in the world in the last five decades. Energy crises lead to deterioration of international relations, economic crises, changes in monetary systems and social problems in countries. This paper aims to show the essential determinants of energy crises by developing a binary logit model which estimates the predictive ability of thirteen indicators in a sample that covers the period from January 1973 to December 2022. The empirical results show that the energy crises are mainly due to energy supply-demand imbalances (petroleum stocks, fossil energy production-consumption imbalance and changes in energy imports by country groups), energy investments (oil and natural gas drilling activities), economic and financial disruptions (inflation, dollar index, index of global real economic activity) and geopolitical risks. Additionally, the model is capable of accurately predicting the world energy crisis event months with 99% probability.

Our analysis of previous studies shows that solar energy and wind can consume negligible amount of water to generate one megawatt of electricity.

Dark energy and dark matter are described as different manifestations of vacuum energy using a newly developed framework of Extra Dimensional Symmetry (EDS) that doubles large spatial dimensions with microscopic partners. In this framework, the bare vacuum energy component exists in a gravitationally dark state where actual gravitational constant G_o = 0G, while real particles oscillate between this inert state and an active state where G_o = 2G. A small component of vacuum energy is constrained to exist in the gravitationally active state as dark energy due to energy density constraint and speed limit asymmetry determined by an asymmetry parameter . Furthermore, neutrinos induce small component of the gravitational constant in the dark state within a volume of space described by the wavelength, enabling the gravitation of virtual particles which manifests as dark matter. The resulting has oscillating light and heavy terms. The heavy term is usually inactive due to its suppression by negative pressure and becomes active for a lifetime once shear stress exceeds negative pressure threshold. This results in a light form of dark matter and a heavy form of dark matter that is detectable through its increased gravitational interaction and resulting acceleration.

In the context of the energy transition, the integration of land use considerations into energy planning is increasingly vital, especially in scenarios where land availability is a constraint. This study addresses the challenge of incorporating land-use aspects into Energy System Optimization Models (ESOMs), with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. Findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.

In order to study the strength changes of concrete specimens under different loading conditions, based on the principle of minimum energy dissipation and from the Angle of concrete failure energy per unit area, the finite element numerical simulation software was applied to concrete specimens with different failure modes of tension, compression, bending and torsion, and the double-broken line damage constitutive model was adopted to simulate and analyze the failure forms of concrete specimens under different loading conditions. The failure area and failure energy of each specimen during loading were quantitatively calculated under each failure mode, and the relationship between the failure area and failure energy consumed under different failure modes was analyzed. The results show that under different failure modes, the failure area of concrete specimens is different, the energy consumed during failure is different, and the strength is different. However, in the process of failure, no matter how the failure mode changes, the failure energy W per unit area remains unchanged and is a constant, which is related to the physical properties of concrete itself.

Higher concentrations of greenhouse gases resulting from anthropogenic actions associated with energy generation are one of the causes of climate change. In view of this, several efforts have been undertaken in the search for more sustainable alternatives, and photovoltaic (PV) technology has stood out among the different possibilities. However, PV generation is highly sensitive to future climate variability, which is a source of uncertainty that can complicate energy planning and compromise the viability of systems. This theme has received attention from the academic community, but some challenges to map and identify relevant literature have been encountered. Therefore, this study was conducted to analyze and identify relevant aspects of international scientific production on the impacts of climate change on the potential of photovoltaic production, CC-PVP, through bibliometric techniques. For this, 3900 articles from the Web of Science and Scopus databases, published between 1960 and 2021, were retrieved and analyzed through a bibliometric approach, using the SciMAT tool. Among the results obtained, it is worth pointing out that the CC-PVP research field (i) has moderate maturity, (ii) is concentrated in the areas of energy, fuels and technology, as well as environmental sciences and meteorology, (iii) has the most studied themes currently related to energy and the forecasting of photovoltaic energy production and electric energy consumption in the world, especially when considering climate change, and (iv) is more researched by Chinese, North Americans and Australians.

This study investigates the relationship between energy metals and precious metals to assess their suitability as safe haven assets in clean energy investment portfolios. It focuses on the impact of events in 2020 and 2022, characterized by substantial investments in clean energy. The research reveals that, except for Nickel Futures (NICKELc1), energy metals are positively linked with clean energy indexes. This suggests that they can serve as a secure investment option for green investors looking to diversify their portfolios. As a result, the study dismisses the initial question, indicating that energy metals can indeed be considered safe havens within the context of clean energy investments. Additionally, the research reinforces prior findings that precious metals like gold, silver, and platinum possess safe haven characteristics in relation to specific clean energy stock indexes. These results have significant implications, particularly given the increasing investments in clean energy stocks and the recurring uncertainties in the market. In summary, this study supports the idea that both energy metals and precious metals can play valuable roles in clean energy portfolios, providing stability during turbulent times in the market.

The paper concludes that the choice of charging strategy depends on the specific requirements and limitations of the off-grid solar PV system, and that a careful analysis of the factors that affect performance is necessary to identify the most appropriate approach. Also provides valuable insights for designing and optimizing off-grid solar PV systems, which can help to improve the efficiency, reliability, and cost-effectiveness of these systems.

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. Single-, double-, and PV module models are investigated to test ICO's parameter estimation performance. Statistical analysis uses minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared to other state-of-the-art optimization algorithms, the ICO algorithm is proven to be highly reliable and accurate when identifying PV parameters.

Solar pavement technology has numerous benefits for advanced life and environmental protection, including reduced fossil fuel usage, reduction harmful emissions from conventional electricity generation and handy reliable electricity via solar energy. This paper aims to find an alternative for cost effective renewable energy sources is in conjunction with the principles of sustainability. This research studies whether the application of thermal energy harvesting road pavement using sustainable conductive piping such as stainless steel, copper, and aluminium are feasible in Malaysia. The scope of the paper is to optimize the suitable parameters for different materials of thermal energy harvester such as pipe material, pipe depth inserted into pavement, types of pipe arrangement, pipe spacing, and flow rate for maximum thermal energy extraction using numerical simulation. The simulation is testing under engineering software ANSYS Workbench 19.2 (Fluent) and Solidworks 2020 to develop project prototype from optimize thermal energy harvester. Thus, serpentine copper pipe shows the highest heat efficiency which is 32.22% with the temperature outlet of 327.35K (54.21°C) at the pipe located at 50 mm from the surface pavement with the pipe spacing of 80 mm centre-to-centre.

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid engine, known as PHEV, which combines two functionalities: the vehicle's internal combustion engine (ICEV) and the electric engine (BEV). The paper also presents the assumptions of the DEVS model, which allows for the estimation of vehicle’s energy consumption. The study described herein also presents the influence of factors such as ambient temperature and constant speed on the PHEV’s energy consumption. It has been shown that the vehicle’s range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in every-day driving conditions. When analyzing the energy consumption based on the vehicle’s unit energy consumption model, attention was paid to values that are strongly correlated with the traffic volume and weather conditions affecting the moving vehicle. In addition, the authors emphasized that the estimated total energy consumption of an electric (BEV) or hybrid (PHEV) vehicle, relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. In extreme cases, the actual range of a hybrid vehicle is 75% shorter.

The objective of the present work is to investigate the performance of flat-plate solar panels in Greece that continuously follow the daily motion of the Sun. To that end, the annual energy sums are estimated on such surfaces from hourly solar horizontal radiation values at 43 locations covering all of Greece. The solar horizontal radiation values are embedded in the typical meteorological years of the sites obtained from the PV-GIS tool. All calculations use a near-real surface albedo; an isotropic and an anisotropic model are used to estimate the diffuse-inclined radiation. The analysis provides regression equations for the energy sums as a function of time (month, season). The annual energy sums are found to vary between 2247 kWhm−2 and 2878 kWhm−2 under all-sky conditions with the anisotropic transposition model. Finally, maps of Greece showing the distribution of the annual and seasonal solar energy sums under all- and clear-sky conditions are derived for the first time.

This paper presents the goals and components of a quantitative energy balance assessment framework to define PEDs flexibly in three important contexts: the context of the district's density and RES potential, the context of a district's location, induced mobility and the context of the dis-trict's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition: system boundaries, balancing weights and balance targets and argue how they can be linked to the definition goals in detail. In particular we specify three levels of system boundaries and argue their individual necessity: operation, including everyday mobili-ty, including embodied energy and emissions. It argues that all three pillars of PEDs, energy effi-ciency, onsite renewables and energy flexibility can be assessed with the single metric of a prima-ry energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the sur-rounding energy system, which we identify as a "context factor". Three examples of such context factors, each corresponding to the balance target of one of the previously defined system bounda-ries operation, mobility and embodied emissions are presented: Density (as a context of opera-tion), sectoral energy balances and location (as a context for mobility) and an outlook of a person-al emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

Transportation, environmental conditions, quality of human life within smart cities, and system infrastructure have all needed practical and dependable smart solutions as urbanization has accelerated in recent years. In addition, the emerging Internet of Things (IoT) provides access to a plethora of cutting-edge, all-encompassing apps for smart cities, all of which contribute significantly to lowering energy consumption and other negative environmental impacts. For smart cities to meet the challenge of using less energy, the authors of this research article suggest planning and implementing an integrated power and heat architecture that puts renewable energy infrastructure and energy-storage infrastructure at the top of the list. To address these issues, we describe a smart proposed NEOSRD architecture that uses a distributed smart area domain to optimize renewable demand energy in a smart city across a wide area network. The energy requirements of desalination procedures are negligible when compared to the total local energy consumption and transportation, a feat accomplished by the proposed NEOSRD system. Here, the computational model shows how the established system is a valuable response to our problems and a cost-effective strategy for creating smarter structural elements that cut down on overall smart cities' energy costs.

As Alberta increases solar power generation, land use conflicts with agriculture increase. A solution that enables low-carbon electricity generation and continued (in some cases increased) agricultural output is the co-locating of solar photovoltaics and agriculture: agrivoltaics. This study reviews policies that impact the growth of agri-voltaics in Alberta. Solar PV-based electricity generation is governed by three regula-tions based on system capacity. In addition, agrivoltaics falls under various legisla-tions, frameworks, and guidelines for land utilization. These include Land Use Frame-work, Alberta Land Stewardship Act, Municipal Government Act, Special Areas Dis-position, Bill 22 and other laws/policies all of which are reviewed in the context of agrivoltaics. Several policies are recommended to support rapid diffusion of agrivolta-ics. First, open access research into agrivoltaics, which not only will help optimize agrivoltaic systems for the region, but also coupled with public education is expected to galvanize social acceptability of large-scale PV deployment. Clearly defining and categorizing agrivoltaic technology, developing agrivoltaic standards, making agri-voltaic technology-friendly regulations/frameworks and developing programs and pol-icies to incentivize agrivoltaic deployment over conventional PV will all accelerate dif-fusion. Through these measures, Alberta can achieve conservation and sustainability in food and energy sector while simultaneously addressing the renewable energy and climate-related goals.

In Europe, the recent application of regulations oriented to zero-energy buildings and climate neutrality in 2050 has led to a reduction in energy consumption for heating and cooling in the construction sector. The thermal insulation of the building envelope plays a key role in this process and the requirements about the maximum allowable thermal transmittance are defined by country-specific guidelines. Typically, high insulation values provide low energy consumption for heating, however, they may paradoxically imply the risk of overheating in summer period and thus negatively affect the overall performance of the building. In addition, the embodied energy and related emissions caused by the manufacturing and transportation processes of thermal insulation cannot be further neglected in the evaluation of the best optimal solution. Therefore, this paper aims to evaluate the influence in terms of embodied and operational energy of various walls’ thermal insulation thicknesses on residential buildings in Europe. To this end, the EnergyPlus engine was used for the energy simulation within Ladybug & Honeybee tools, by parametrically conducting multiple iterations; 53 variations of external wall U-value, considering high and low thermal mass scenarios, were simulated for 100 reference cities of the European context, using a representative multifamily building as a reference. The results demonstrate that massive walls generally perform better than lightweight structures and, of course, the best solution in terms of energy varies according to each climate. The optimal values are graphically reported on the map of Europe according to specific climatic features, providing a guidance for new constructions and building retrofit.

This study reports an empirical analysis of an all-electric, Net Zero Energy Housing (NZEH) development located in a mixed-humid climate zone (4A, Virginia, USA). Circuit-level energy monitors were used to measure energy consumption and energy production data (solar photovoltaic) at 1-hr intervals in six identical apartments over 24 months. The study employs a multi-step case study methodology to a) empirically evaluate energy consumption and production data, b) identify the temporal variability of energy consumption and production data at different time scales, c) understand the impact(s) of weather and human-building interaction on energy consumption and production, and d) synthesize the study’s “lessons learned” toward data-driven recommendations for future NZEH researchers and practitioners. The study found that the development’s net zero energy goal was achieved in three of six case units and that NZEH housing performance was more influenced by human-building interaction than weather variability. The analysis also found the solar photovoltaic (PV) performance to be reliable across the sampled units over the periods of measurement, suggesting that solar PV could be oversized as an approach to overcome verifiability in HBI and achieve NZEH performance goals.

Indonesia is an interesting case study for students of sustainable development and sustainable energy due to its ability to connect the multiple “worlds” it has become part of. Indonesia is an important bridge to Muslim countries, the voice of the Global South in the G20 and a main pillar of the 134-country-strong G77. Indonesia’s development trajectory is also key to the achievement of the Paris Climate Agreement as well as of the 2030 Agenda. Students can learn from how Indo-nesia address contradictions that would have been unsurmountable for other countries. Indone-sia’s energy transition offers helpful lessons, because of its aspiration to become a developed country by 2045. This goal is only possible when a country is able to effectively address barriers and caveats to sustainable energy. It is interesting how Indonesia focuses on silver linings and come up with pragmatic solutions to energy-related issues. This is followed by the “teaching guide,” which provides recommendations how the lessons from Indonesia can be embedded into a learning experience. The “learning activation approach” is introduced, which encourages stu-dents to systematically reflect on the complexity of selected contexts and understand this com-plexity by looking at the technical issues and processes that allow decision-making.

This paper provides an open dataset of measured energy use, solar energy production, and building air leakage data from a 328 m² (3,531 ft²) all-electric, zero energy commercial building in Virginia, USA. Over two years of energy use data were collected at 1-hour intervals using circuit-level energy monitors. Over six years of solar energy production data were measured at 1-hour intervals by 56 microinverters. The building air leakage data was measured post-construction per ASTM-E779 Standard Test Method for Determining Air Leakage Rate by Fan Pressurization and the United States Army Corps (USACE) Building Enclosure Testing procedure; both pressurization and depressurization results are provided. The architectural and engineering (AE) documents are provided to aid researchers and practitioners in reliable modelling of building performance. The paper describes the data collection methods, cleaning, and convergence with weather data. This dataset can be employed to predict, benchmark, and calibrate operational outcomes in zero energy commercial buildings.

A reliable and affordable energy supply is a fundamental prerequisite for reducing poverty, promoting investment, and boosting economic growth in the developing world. Among the different challenges that developing countries face, chronic energy crises are harrowing. The crises result from the unsatisfactory state of the central grid, a misguided energy mix, and ill-informed policies, among other things. The possibility of solving energy crises through a variety of alternative solutions is worth exploring. This review discusses two paths of energy development side by side: a traditional “hard” path of energy development (i.e., central grid extension powered by fossil fuels and nuclear energy expansion) and a relatively recent “soft” path of energy development, which is based on energy conservation and the deployment of renewable energy resources. This paper focuses on one central axis of the discussion: centralization vs. decentralization. This discussion, in turn, has technological, economic/business, and political dimensions. Finally, the paper discusses the significance of the debate from meeting the developing world’s energy demands. The paper intends not to prefer one or another path of energy development, nor it gives recommendations on diffusing or adopting those development paths. Instead, it explores the literature’s central arguments that might help frame the questions for further research. While this debate could be used to ask interesting questions that might help solve the energy crisis in the developing world, the discussion informs countries to advance policies specific to their circumstances under the umbrella of a sound and thoughtful energy productivity policy framework.

The measures for tackling the COVID-19 may shrink the global GDP by approximately 6% in 2020, the deepest post-war recession. As a result, the global energy demand declined by 3.8% in the first quarter of 2020. Concerning fossil fuels, this conjuncture reduced the demand drastically and collapsed the prices to historic levels. Despite the general market disruptions, renewable energy sources (RES) seem to be more resilient to the crisis because they are the only sources that will grow in demand in 2020, driven by priority dispatch. The RES´s significant growth in cumulative installed capacity in the last two decades and the significant cost reductions of RES and energy storage technologies are positive signs towards better market conditions for the global energy transition. Currently, the crisis is seen by international agencies and transition scholars as an opportunity to advance a renewable-based energy transformation. Nevertheless, this article aims at caution about another possibility: if societal changes are not urgently implemented, the crisis may weaken the global energy transition. This article examines this last possibility from a three-level perspective: 1) post-COVID economic recovery, 2) low oil and natural gas prices and competitiveness of alternative sources and, 3) reorganization of the world energy market and the OPEC+. This paper exists to stimulate debate.

For a hierarchy of large scale structures, like galaxies, galaxy clusters, super-clusters, etc. the requirement that their gravitational (binding) self-energy density must at least equal or exceed the background repulsive dark energy density implies a universal mass-radius relation given by M/R² ~ 1g/cm². Here we point out that this mass-radius relation also holds for important individual biological structures, and even various organisms as a whole such as humans, trees, and to behemoths like whales, even though the physics is different. Here we try to understand this universality from two different physical considerations. We also point out the coincidence that the densities of most biological entities are close to that of water, and that the average density of main-sequence stars and giant planets are close to this density. We give a physical basis for the same.

The threats of climate change and the sustainable supply of clean energy are global challenges that require an international approach to the energy supply. Utilizing the wind and solar energy potential of regions where these renewable sources are especially viable to produce hydrogen by means of water electrolysis represents an attractive option to counter the above-mentioned challenges. Within the scope of this techno economic analysis of a worldwide hydrogen supply infrastructure based on renewable energy, selected regions are assessed on the basis of their wind or solar energy potential. In contrast to established analyses of hydrogen infrastructures, this paper introduces a worldwide allocation approach to the supply hydrogen from strong wind and solar regions to different demand regions on the premise of a global supply cost minimum. The allocation results show a significant dependence of hydrogen export volumes and the oversea transport distances of potential trading partners. Hence, the transnational trading flows of hydrogen derived from wind and solar energy are concentrated in continental regions.

The transition towards a renewable energy system is essential in order to reduce greenhouse gas emissions. The increase in the share of variable renewable energy sources (VRES), which mainly comprise wind and solar energy, necessitates storage technologies by which the intermittency of VRES can be compensated for. Although hydrogen has been envisioned to play a significant role as a promising alternative energy carrier in a future European VRES-based energy concept, the optimal design of this system remains uncertain. In this analysis, a hydrogen infrastructure is posited that would meet the electricity and hydrogen demand for a 100% renewable energy-based European energy system in the context of 2050. The overall system design is optimized by minimizing the total annual cost. Onshore and offshore wind energy, open-field photovoltaics (PV), rooftop PV and hydro energy, as well as biomass, are the technologies employed for electricity generation. The electricity generated is then either transmitted through the electrical grid or converted into hydrogen by means of electrolyzers and then distributed through hydrogen pipelines. Battery, hydrogen vessels and salt caverns are considered as potential storage technologies. In the case of a lull, stored hydrogen can be re-electrified to generate electricity to meet demand during that time period. For each location, eligible technologies are introduced, as well as their maximum capacity and hourly demand profiles, in order to build the optimization model. In addition, a generation time series for VRES has been exogenously derived for the model. The generation profiles of wind energy have been investigated in detail by considering future turbine designs with high spatial resolution. In terms of salt cavern storage, the technical potential for hydrogen storage is defined in the system as the maximum allowable capacity per region. Whether or not a technology is installed in a region, the hourly operation of these technologies, as well as the cost of each technology, are obtained within the optimization results. It is revealed that a 100 percent renewable energy system is feasible and would meet both electricity demand and hydrogen demand in Europe.

As the energy efficiency demands for future buildings become increasingly stringent, preliminary assessments of energy consumption are mandatory. These are possible only through numerical simulations, whose reliability crucially depends on boundary conditions. We therefore investigate their role in numerical estimates for the usage of geothermal energy, performing annual simulations of transient heat transfer for a building employing a geothermal heat pump plant and energy piles. Starting from actual measurements, we solve the heat equations in 2D and 3D using COMSOL Multiphysics and IDA-ICE, and discover a negligible impact of the multiregional ground surface boundary conditions. Moreover, we verify that the thermal mass of the soil medium induces a small vertical temperature gradient on the piles surface. We also find a roughly constant temperature on each horizontal cross-section, with nearly identical values if the average temperature is integrated over the full plane or evaluated at one single point. Calculating the yearly heating need for an entire building we then show that the chosen upper boundary condition affects the energy balance dramatically. Using directly the pipes’ outlet temperature induces a 54% overestimation of the heat flux, while the exact ground surface temperature above the piles reduces the error to 0.03%.

Power system decentralization has been an emerging topic for the past decade in an effort to improve power quality and environment protection via increased integration of renewable energy sources. Towards these objectives, decentralized microgrids have been proposed and thoroughly investigated in terms of technical capabilities and economic performance. In fact, the planning and actual operation of small-scale, decentralized microgrids has started in countries such as Canada, Japan, USA, UK and other countries. It is expected that the research in this area will progress and eventually take over the existing paradigm of large-scale power generation in the future. These small-size decentralized microgrids could be connected with nearby microgrids under normal operating conditions, but under special events, such as natural or man-made disasters, they would be disconnected and run in islanded mode. Under such high impact – low probability events, the microgrid must have resiliency to successfully re-connect with other microgrids and the main grid. In this paper, an Energy Management System (EMS) for a microgrid having a resiliency function, allowing to operate under islanded mode after an accident, is proposed. The proposed tool, called Resilient Energy Management System (ResEMS), aims at procuring reserve power into the microgrid’s Battery Energy Storage System (BESS) effectively, by importing it from the nearby connected power system. The accident is assumed to be a predictable natural disaster, which means that the accident occurrence time, duration and level of damage can be estimated. The proposed ResEMS has been applied to a microgrid comprising of a BESS, a diesel generator and several photovoltaic devices. The simulation results verify its beneficial operation.

The There are many factors influencing the performance of photovoltaic (PV) systems. Among these factors, temperature and solar radiation are two major parameters that have a large effect on the efficiency of PV systems. The cell temperature of PV panels is related to the ambient temperature while the solar radiation incident on the surface of the PV modules depends on the slope and azimuth of these modules. Furthermore, ground reflectance (albedo) affects the irradiance incident on the PV panel surface, which in turn affects the output of a PV system. Nevertheless, the effects of these factors on the economic performance of the solar PV systems are scarcely reported. This paper presents a complete design of a stand-alone PV/battery system to supply electric power for a mobile base station in Choman, Erbil, Iraq. The effects of different factors on the total electricity produced by PV arrays and its economic performance are simultaneously investigated. HOMER software has been used as a tool for the techno-economic and environmental analysis. As indicated from the simulation results, the PV array capacity and its economic performance are highly affected by the variation of the slope and azimuth. With a base case (albedo of 20% and average annual ambient temperature of 11°C), the best feasible system which is achieved by facing PV due to south with a tilt angle of 40° or 45°, is found to have net present cost (NPC) of 70595 $ and cost of energy (COE) of 0.54 $/kWh. Moreover, the results indicate that increasing the ground reflectance from 10% to 90% results in a 7.2% decrease in the PV array capacity and about 3% decrease in the NPC and COE. On the other hand, increasing the ambient temperature from 0°C to 40°C results in a 19.7% increase in the PV array capacity and an 8.2% increase in the NPC and COE. Furthermore, according to the ambient temperature of Choman, using PV modules with high sensitivity to temperature is found to be an attractive option. Provided simulation performance analysis proves that the studied parameters must be treated well to establish an enabling environment for solar energy development in Iraq.

Undernutrition and micronutrient deficiencies such as anemia are considered significant public health challenges in Bangladesh, which enhancing fish consumption is a well-established food-based intervention to address these. This paper documents the establishment of community-based fish chutney production and reports the impact of its consumption on mid-upper arm circumference (MUAC) and hemoglobin (Hb) levels among targeted 150 pregnant and lactating women (PLW) in rural Bangladesh. A fish chutney was developed using locally available ingredients followed by a series of laboratory tests, including nutrient composition, shelf-life and food safety. A community-based fish chutney production process was designed to: 1) supply locally available ingredients for processing; 2) establish two fish drying sites; 3) initiate a community-based production site, and 4) distribute fish chutney to PLW by six women nutrition field facilitators for one year. Then a pre- and post-intervention study was designed for a selected 150 PLW to receive 30g of fish chutney daily for 12 months. Differences in mean mid-upper arm circumference (MUAC) and hemoglobin (Hb) levels pre- and post-consumption were analyzed using one-way analysis of variance. Consumption of 30g of fish-chutney resulted in significant increases of the mean values of Hb levels and MUAC among the targeted PLW.

It's a method to achieve energy savings in single-phase active power greater than that obtained with the "Fan Law" in electric machines applied to ventilation. Quantitative analysis methods were based on electrotechnical techniques, practiced with the corresponding laboratory equipment on the working materials (three prototypes of electric machines). The results obtained from experimentation on the test bench were recorded in tables that collect data of formulas, values, and physical units. The discussion provides a comprehensive comparative study, mainly between power (watts), active energy consumption (kWh), and rotational speed (revolutions per minute). The permanent magnet synchronous motor (PMSM) with the coupling of an RL circuit design does mechanical work at its maximum speed of 3000 (RPM) with only 6.3 (Watts), which is equivalent to 25.2% of the active power required by the single-phase induction asynchronous motor (or shaded pole motor), which required 25 (Watts) to rotate at 1690 (RPM). This translates to 75% lower active power with a 44% superiority in speed, resulting in a 75% savings in single-phase active energy (measured in kWh). The same occurs when comparing it to the universal AC motor (with brushes and wound rotor) to maintain a speed of 3000 (RPM); since it consumes 64.8 (Watts), which is 90.3% more single-phase active energy than that required to achieve the same rotational speed as the permanent magnet synchronous motor (PMSM). The study was conducted on all motors with the same impeller blade diameter and under identical air temperature and atmospheric pressure conditions.

In the traditional power system, customers respond to their primary electricity consumption pattern based on price or incentive to take additional advantages. By developing energy hubs (EHs) where electricity, heat, natural gas and other forms of energy are coupled together, all types of energy customers even the inelastic loads can participate in demand response (DR) program. This novel vision has led to the concept of "integrated demand response (IDR)". IDR programs (IDRPs) in EHs involve coordinating multiple DR activities across different energy systems, such as buildings, industrial complexes, and transportation networks. The main purpose of IDR is that multi-energy users can respond not only by shifting or reducing energy consumption from the demand side, but also by changing the type of energy consumed, in response to the dispatching center. The integration of IDRPs in EHs can help to reduce energy costs, improve grid stability, and increase the penetration of renewable energy sources (RES) in the power system. Moreover, by synchronizing DR activities across different energy systems, IDRPs can provide additional benefits, such as improved energy efficiency, reduced greenhouse gas emissions, and increased resilience to power outages and other disruptions. In this paper, we provide a review, assessment, and classification of fundamental principles, modeling techniques and optimization methods for IDR programs in EHs.

This paper is devoted to an accurate determination of the London dispersive and polar free energy of adsorption, the two Lewis acid γs+ and base γs- components of polar surface energy γsAB of 2D single-crystalline and polycrystalline covalent organic frameworks such as TAPPy-TPA-COFs. Inverse gas chromatography (IGC) at infinite dilution was used to quantify the different surface parameters of the different materials. From IGC measurements, one determined the net retention time of the adsorption of n-alkanes and several polar solvents on single-crystalline and polycrystalline covalent organic frameworks. The free surface Gibbs energy of adsorption was obtained for the various organic molecules at different temperatures from their net retention volume values. The separation between the London dispersive energy and the polar energy of adsorbed molecules was carried out by using a new thermodynamic parameter PSX chosen as new indicator variable and taken into account the deformation polarizability and the harmonic mean of the ionization energies of solvents and solid materials, derived from London dispersion equation. The obtained results are very promising for the accurate determination of the surface thermodynamic parameters of adsorption of organic solvents on solid surfaces.

The need to address climate change and establish sustainable urban environments has driven increased efforts in Europe to develop climate-neutral cities. This study highlights the importance of integrating solar energy technologies and nature-based solutions as key strategies to achieve climate neutrality. By examining current practices, emerging trends, and case examples, it explores the benefits, challenges, and prospects associated with the integration of solar energy and nature-based solutions in urban contexts. The study presents a pioneering approach to assess the urban heat and climate change mitigation benefits of combining building-integrated photovoltaics and nature-based solutions specifically within the European context. The results emphasize the synergistic relationship between nature-based components and solar conversion technology, identifying effective combinations for different climatic zones. In warmer regions of Southern Europe, strategies like rooftop photovoltaics on cool roofs, photovoltaics shadings, green walls, and urban trees have demonstrated effectiveness. Conversely, mid- and high-latitude European cities have seen positive impacts through the integration of rooftop photovoltaics and photovoltaics facades with green roofs and green spaces. As solar cell conversion efficiency improves, the environmental impact of photovoltaics is expected to decrease, facilitating their integration into urban environments. The study emphasizes the importance of incorporating water bodies, cool pavements, spaces with high sky-view factors, and effective planning in urban design to maximize resilience benefits. It also highlights the significance of prioritizing mitigation actions in low-income regions and engaging citizens in the development of social photovoltaics positive energy houses, resilient neighbourhoods, and green spaces. By adopting these recommendations, European cities can lead the way in creating climate-neutral urban environments that prioritize clean energy, nature-based solutions, and the overall well-being of residents. The findings underscore the need for a multidisciplinary approach that combines technological innovation, urban planning strategies, and policy frameworks to effectively achieve climate neutrality in European cities.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

This paper presents an approach to the application of the Model-Based Systems Engineering (MBSE) and Model-Based Systems Architecting (MBSA) principles to develop a Model-Based Pattern Language (MBPL). It takes too long for systems engineers and architects to develop a new system from scratch, particularly new space-based systems derived from the existing space systems architectures. A pattern language is a holistic view of reusable logical model artifacts; many are interdisciplinary and introductory, if at all. The results are mostly a combination of the application-specific logical solution, which further results in the best possible overall solution. The main benefit of the pattern language is reducing the time and validation required to generate a new space-based system architecture; this approach will develop top-level requirements in the initial phase of the system development. The rationale of the methodology proposed by the paper is as follows, collect, and decompose published literature and other open-source information available on space system architectures and system models; develop SysML models for systems, subsystems, products, assembly, subassembly level, and mission-specific requirements using CAMEO SysML software. Arrange these patterns to develop a functional ontology and construct a logical architecture pattern library. This approach created, updated, and managed SysML pattern language, which evaluated the expedited new model construction. Again, our objective is to develop a logical pattern language using public domain information and evaluate patterns by constructing a new space mission concept—for example, planetary surface habitat.

Sustainable energy development (SED) is a crucial component of the Sustainable Development Goals (SDG), aiming to maintain economic and social progress while protecting the environment and mitigating climate change’s effects. SED serves as a transition paradigm for sustainable development, providing a blueprint for energy peace and prosperity for people and all uses. This article presents the history of SED and then uses a critical discourse approach to summarize existing review studies in SED. Ten interlinked themes of SED are identified, with two of them considered to be among the least studied in existing SED reviews and in the current global discussion around climate change. This study explores these two themes, which include energy financing and the need for 100% renewable energy (RE), a sub-theme of decarbonization strategy working towards the 1.5–2.0 °C scenario. The study suggests that the current G20 countries’ contributions, if maintained continuously per annum, in addition to 80% more funding from private investment compared to the amount in the 1.5 °C scenario financial requirements for clean energy, are sufficient to limit global warming. In addition to the present drive for 100% RE, the article also discusses emerging issues, such as energy storage options with an indication of hydrogen as the most promising, other energy-related development agendas, and the need for regional security stability to prevent energy wars. Selected SED decarbonization strategies are presented across the power, transport, building, and industrial sectors. The study concludes with progress and directions for future research, mainly the need for re-defining nationally determined contribution (NDC) through an emissions budgeting and centralized global or regional emissions stock-taking strategy working towards the 1.5 °C scenario.

Conventional methods of climate change (CC) mitigation have not ‘bent the curve’ of steadily rising annual anthropic CO2 emissions or atmospheric concentrations of greenhouse gases. This study reviews the present position and likely future of such methods, using recently published literature with a global context. It particularly looks at how fast they could be implemented, given that the limited time available for avoiding catastrophic CC (CCC). The study then examines solar geoengineering, an approach often viewed as complementary to conventional mitigation. The review next introduces equity considerations, and shows how this will shorten even further the time available for effective action for CC mitigation. The main findings are as follows. Conventional mitigation approaches will be implemented too slowly to be of much help in avoiding CCC, partly because some suggested technologies are infeasible, while others are either of limited technical potential, or, like wind and solar energy, cannot be introduced fast enough. Because of these problems, solar geoengineering is increasingly advocated as a quick-acting and effective solution. However, it could have serious side effects, and given that there will be winners and losers at the international as well as the more regional level, political opposition may make it difficult to implement. The conclusion is that global energy consumption itself must be rapidly reduced to avoid catastrophic climate change.

Background: Mitigating climate change requires fundamentally redesigned energy systems in which renewable energy sources ultimately replace fossil fuels such as natural gas. In this context, the question how and to which extent the gas sector can contribute to an increasingly climate-neutral future EU energy system is heavily debated among scholars, energy industry experts and policy makers. Methods: We take a two-step approach: We begin with a review of studies from energy industry and academia to discuss potential gas sector contributions from a holistic energy system design point of view; this is followed by a comprehensive discussion of technical potentials, micro-economic conditions and societal implications of renewable gas. We then enrich our findings with the results of an empirical focus group process. Results: The gas sector can not only contribute to balancing volatile renewable energy production but also enable the supply of renewable energy to end-users in gaseous form; based on existing infrastructure. This could reduce costs for society, increase public acceptance and ultimately speed up the energy system transformation. There is the theoretical technical potential to substitute major parts of natural gas with renewable gas of biogenic and synthetic nature. This, however, crucially requires a supportive policy framework like the one established for renewable electricity. Conclusion: Given the societal benefits and the competitiveness of renewable gas as compared to renewable alternatives, energy policy makers should incorporate renewable gas and the existing gas infrastructure in the future energy system framework. The objective should be an optimized interplay of various energy vectors and their infrastructure along the entire energy supply chain. This requires a level playing field for different renewable technologies across different policy areas and a form of public support that strikes the balance between facilitating the gradual substitution of natural gas by renewable gas while maintaining public acceptance for this transformation despite higher costs for end-users.: Given the societal benefits and the competitiveness of renewable gas as compared to renewable alternatives, energy policy makers should incorporate renewable gas and the existing gas infrastructure in the future energy system framework. The objective should be an optimized interplay of various energy vectors and their infrastructure along the entire energy supply chain. This requires a level playing field for different renewable technologies across different policy areas and a form of public support that strikes the balance between facilitating the gradual substitution of natural gas by renewable gas while maintaining public acceptance for this transformation despite higher costs for end-users.

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogues market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.