J — Multidisciplinary Scientific Journal

Local energy networks, known as microgrids, can operate independently or in conjunction with the main grid, offering numerous benefits such as enhanced reliability, sustainability, and efficiency. This study focuses on analyzing the factors that influence energy performance in East-West microgrids, which have the unique advantage of capturing solar radiation from both directions, maximizing energy production throughout the day. A predictive pipeline was also developed to assess the performance of various machine learning models in forecasting energy output. Key input data for the models included solar radiation levels, photovoltaic (DC) energy, and the losses incurred during the conversion from DC to AC energy. One of the study’s significant findings was that the east side of the microgrid received higher radiation and experienced fewer losses compared to the west side, illustrating the importance of orientation for efficiency. Another noteworthy result was the predicted total energy supplied to the grid, valued at €15,423. This demonstrates that the optimized energy generation not only meets grid demand but also generates economic value by enabling the sale of excess energy back to the grid. The machine learning models—Random Forest, Extreme Gradient Boosting, and Recurrent Neural Networks—showed superior performance in energy prediction, with mean squared errors of 0.000318, 0.000104, and 0.000081, respectively. The research concludes that East-West microgrids have substantial potential to generate significant energy and economic benefits. The developed energy prediction pipeline can serve as a useful tool for optimizing microgrid operations and improving their integration with the main grid. Full article

Quantum-secure cryptography is a dynamic field due to its crucial role in various domains. This field aligns with the ongoing efforts in data security. Post-quantum encryption (PQE) aims to counter the threats posed by future quantum computers, highlighting the need for further improvement. Based on the learning with error (LWE) system, this paper introduces a novel asymmetric encryption technique that encrypts entire messages of n bits rather than just 1 bit. This technique offers several advantages including an additive homomorphic cryptosystem. The robustness of the proposed lightweight public key encryption method, which is based on a new version of LWE, ensures that private keys remain secure and that original data cannot be recovered by an attacker from the ciphertext. By improving encryption and decryption execution time—which achieve speeds of 0.0427 ms and 0.0320 ms, respectively—and decreasing ciphertext size to 708 bits for 128-bit security, the obtained results are very promising. Full article

The COVID-19 pandemic is caused by a novel and rapidly mutating coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although several drugs are already in clinical use or under emergency authorization, there is still an urgent need to develop new drugs. Through the mining and analysis of 2776 genomes of the SARS-CoV-2 virus, we identified papain-like protease (PLpro), which is a critical enzyme required for coronavirus to generate a functional replicase complex and manipulate post-translational modifications on host proteins for evasion against host antiviral immune responses, as a conserved molecular target for the development of anti-SARS-CoV-2 therapy. We then made an infection model using the NCI-H1299 cell line stably expressing SARS-CoV-2 PLpro protein (NCI-H1299/PLpro). To investigate the effect of targeting and degrading PLpro mRNA, a compact CRISPR-Cas13 system targeting PLpro mRNA was developed and validated, which was then delivered to the aforementioned NCI-H1299/PLpro cells. The results showed that CRISPR-Cas13 mediated mRNA degradation successfully reduced the expression of viral PLpro protein. By combining the power of AAV and CRISPR-Cas13 technologies, we aim to explore the potential of attenuating viral infection by targeted degradation of important viral mRNAs via safe and efficient delivery of AAV carrying the CRISPR-Cas13 system. This study demonstrated a virus-against-virus gene therapy strategy for COVID-19 and provided evidence for the future development of therapies against SARS-CoV-2 and other RNA viral infections. Full article

Background: The objective of this study was to compare the facial pattern according to Steiner’s cephalometric analysis with other facial measurement methods (Ricketts, Björk-Jarabak, and McNamara). Methods: 200 patients from a university orthodontic clinic were studied. Measurements were taken using Ricketts, Steiner, Björk-Jarabak, and McNamara methods. Results were compared using standard deviation proportions. Results: Significant differences were found between Steiner’s method and the gold standard. No differences were observed between mixed and permanent dentition groups. Errors were noted in facial type classification: 54.8% in the brachyfacial group, 80% in the mesofacial group and 14.5% in the dolichofacial group. Conclusion: The mandibular angle of Steiner tends to make a diagnosis more towards the dolichofacial type compared to other methods. A protocol is proposed to adjust the value of the mandibular angle of Steiner to the other three methods in a Spanish population. Full article

Haebara and Stocking–Lord linking methods are frequently used to compare the distributions of two groups. Previous research has demonstrated that Haebara and Stocking–Lord linking can produce bias in estimated standard deviations and, to a smaller extent, in estimated means in the presence of differential item functioning (DIF). This article determines the asymptotic bias of the two linking methods for the 2PL model. A bias-reduced Haebara and bias-reduced Stocking–Lord linking method is proposed to reduce the bias due to uniform DIF effects. The performance of the new linking method is evaluated in a simulation study. In general, it turned out that Stocking–Lord linking had substantial advantages over Haebara linking in the presence of DIF effects. Moreover, bias-reduced Haebara and Stocking–Lord linking substantially reduced the bias in the estimated standard deviation. Full article

The discovery of natural products has been pivotal in drug development, providing a vast reservoir of bioactive compounds from various biological sources. This narrative review addresses a critical research gap: the largely underexplored role of gut microbiota in the mediation and biotransformation of medicinal herb-derived natural products for therapeutic use. By examining the interplay between gut microbiota and natural products, this review highlights the potential of microbiota-mediated biotransformation to unveil novel therapeutic agents. It delves into the mechanisms by which gut microbes modify and enhance the efficacy of natural products, with a focus on herbal medicines from Ayurveda and traditional Chinese medicine, known for their applications in treating metabolic and inflammatory diseases. The review also discusses recent advances in microbiota-derived natural product research, including innovative methodologies such as culturomics, metagenomics, and metabolomics. By exploring the intricate interactions between gut microorganisms and their substrates, this review uncovers new strategies for leveraging gut microbiota-mediated processes in the development of groundbreaking therapeutics. Full article

Due to climate change, population increase, and the urban heat island effect (UHI), the demand for cooling energy, especially in urban areas, has increased and will further increase in the future. Technologies such as radiative cooling offer a sustainable and energy-free solution by using the wavelength ranges of the atmosphere that are transparent to electromagnetic radiation, the so-called atmospheric window (8–13 µm), to emit thermal radiation into the colder (3 K) outer space. Previous publications in the field of textile building cooling have focused on specific fiber structures and textile substrate materials as well as complex multi-layer constructions, which restrict the use for highly scaled outdoor applications. This paper describes the development of a novel substrate-independent coating with spectrally selective radiative properties. By adapting the coating parameters and combining low-emitting and solar-reflective particles, along with a matrix material emitting strongly in the mid-infrared range (MIR), substrate-independent cooling below ambient temperature is achieved. Moreover, the coating is designed to be easily applicable, with a low thickness, to ensure high flexibility and scalability, making it suitable for various applications such as membrane architecture, textile roofs, or tent construction. The results show a median daytime temperature reduction (7 a.m.–7 p.m.) of 2 °C below ambient temperature on a hot summer day. Full article

Recently, the United States has experienced, on average, costs of USD 20 billion due to natural and climate disasters, such as hurricanes and wildfires. In this study, we focus on wildfires, which have occurred more frequently in the past few years. This paper examines how various factors, such as the PM10 levels, elevation, precipitation, SOX, population, and temperature, can influence the intensity of wildfires differently across counties in California. More specifically, we use Bayesian analysis to classify all counties of California into two groups: those with more wildfires and those with fewer wildfires. The Bayesian model incorporates prior knowledge and uncertainty for a more robust understanding of how these environmental factors impact wildfires differently among county groups. The findings show a similar effect of the SOX, population, and temperature, while the PM10, elevation, and precipitation have different implications for wildfires across various groups. Full article

Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including recall, F1-score, accuracy, precision, and area under the curve (AUC). The results of the advanced VGG16 model showcased remarkable accuracy, achieving 98.58%. This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector. Full article

Due to global development and increased public awareness of food’s effects on health, demands for innovative and healthy products have risen. Biodegradable and environmentally friendly polymer usage in modern food products is a promising approach to reduce the negative health and environmental effects of synthetic chemicals. Also, desirable features such as flavor, texture, shelf-life, storage condition, water holding capacity, a decrease in water activity, and an oil absorption of fried food have been improved by many polysaccharides. One of the important polymers, which is applied in the food industry, is alginate. Alginates are a safe and widely used compound in various industries, especially the food industry, which has led to innovative methods for for the improvement of this industry. Currently, different applications of alginate in stable emulsions and nano-capsules in food applications are due to the crosslinking properties of alginate with divalent cations, such as calcium ions, which have been studied recently. The main aim of this review is to take a closer look at alginate properties and applications in the food industry. Full article

Background: The lack of knowledge about physical responses to pickleball creates a clear gap about performance in this sport. The purpose of this study was to investigate neuromuscular fatigue caused by playing doubles pickleball. Methods: Recreational pickleball players (n = 32, mean age = 60.0 years) were recruited to perform sets of four countermovement jumps (CMJs) on a force plate before and after doubles pickleball matches. Results: For players who had not played a match prior to testing, there was a significant learning effect across trials within the baseline set of jumps for five outcomes from the CMJ test, including propulsive peak force (p = 0.005); however, there was no significant learning effect for jump height. There were significant improvements in the large effect size for all except one dependent variable (propulsive phase time) between the first and second set of jumps (i.e., after one match). Neither further increases nor decreases were seen after the second set of jumps. Conclusions: Participants saw significant increases in CMJ performance across trials after one pickleball match, indicating learning and potentiation effects. After three matches of doubles pickleball, no fatigue effect was detected. Full article

Collaborative learning has been widely studied in higher education and beyond, suggesting that collaboration in small groups can be effective for promoting deeper learning, enhancing engagement and motivation, and improving a range of cognitive and social outcomes. The study presented in this paper compared different forms of human and robot facilitation in the game of planning poker, designed as a collaborative activity in the undergraduate course on agile project management. Planning poker is a consensus-based game for relative estimation in teams. Team members collaboratively estimate effort for a set of project tasks. In our study, student teams played the game of planning poker to estimate the effort required for project tasks by comparing task effort relative to one another. In this within- and between-subjects study, forty-nine students in eight teams participated in two out of four conditions. The four conditions differed in respect to the form of human and/or robot facilitation. Teams 1–4 participated in conditions C1 human online and C3 unsupervised robot, while teams 5–8 participated in conditions C2 human face to face and C4 supervised robot co-facilitation. While planning poker was facilitated by a human teacher in conditions C1 and C2, the NAO robot facilitated the game-play in conditions C3 and C4. In C4, the robot facilitation was supervised by a human teacher. The study compared these four forms of facilitation and explored the effects of the type of facilitation on the facilitator’s competence (FC), learning experience (LX), and learning outcomes (LO). The results based on the data from an online survey indicated a number of significant differences across conditions. While the facilitator’s competence and learning outcomes were rated higher in human (C1, C2) compared to robot (C3, C4) conditions, participants in the supervised robot condition (C4) experienced higher levels of focus, motivation, and relevance and a greater sense of control and sense of success, and rated their cognitive learning outcomes and the willingness to apply what was learned higher than in other conditions. These results indicate that human supervision during robot-led facilitation in collaborative learning (e.g., providing hints and situational information on demand) can be beneficial for learning experience and outcomes as it allows synergies to be created between human expertise and flexibility and the consistency of the robotic assistance. Full article

This research paper presents a novel approach for occlusion inpainting in thermal images to efficiently segment and enhance obscured regions within these images. The increasing reliance on thermal imaging in fields like surveillance, security, and defense necessitates the accurate detection of obscurants such as smoke and fog. Traditional methods often struggle with these complexities, leading to the need for more advanced solutions. Our proposed methodology uses a Generative Adversarial Network (GAN) to fill occluded areas in thermal images. This process begins with an obscured region segmentation, followed by a GAN-based pixel replacement in these areas. The methodology encompasses building, training, evaluating, and optimizing the model to ensure swift real-time performance. One of the key challenges in thermal imaging is identifying effective strategies to mitigate critical information loss due to atmospheric interference. Our approach addresses this by employing sophisticated deep-learning techniques. These techniques segment, classify and inpaint these obscured regions in a patch-wise manner, allowing for more precise and accurate image restoration. We propose utilizing architectures similar to Pix2Pix and UNet networks for generative and segmentation tasks. These networks are known for their effectiveness in image-to-image translation and segmentation tasks. Our method enhances the segmentation and inpainting process by leveraging their architectural similarities. To validate our approach, we provide a quantitative analysis and performance comparison. We include a quantitative comparison between (Pix2Pix and UNet) and our combined architecture. The comparison focuses on how well each model performs in terms of accuracy and speed, highlighting the advantages of our integrated approach. This research contributes to advancing thermal imaging techniques, offering a more robust solution for dealing with obscured regions. The integration of advanced deep learning models holds the potential to significantly improve image analysis in critical applications like surveillance and security. Full article

Recent advances in all-solid-state battery (ASSB) research have significantly addressed key obstacles hindering their widespread adoption in electric vehicles (EVs). This review highlights major innovations, including ultrathin electrolyte membranes, nanomaterials for enhanced conductivity, and novel manufacturing techniques, all contributing to improved ASSB performance, safety, and scalability. These developments effectively tackle the limitations of traditional lithium-ion batteries, such as safety issues, limited energy density, and a reduced cycle life. Noteworthy achievements include freestanding ceramic electrolyte films like the 25 μm thick Li_0.34La_0.56TiO₃ film, which enhance energy density and power output, and solid polymer electrolytes like the polyvinyl nitrile boroxane electrolyte, which offer improved mechanical robustness and electrochemical performance. Hybrid solid electrolytes combine the best properties of inorganic and polymer materials, providing superior ionic conductivity and mechanical flexibility. The scalable production of ultrathin composite polymer electrolytes shows promise for high-performance, cost-effective ASSBs. However, challenges remain in optimizing manufacturing processes, enhancing electrode-electrolyte interfaces, exploring sustainable materials, and standardizing testing protocols. Continued collaboration among academia, industry, and government is essential for driving innovation, accelerating commercialization, and achieving a sustainable energy future, fully realizing the transformative potential of ASSB technology for EVs and beyond. Full article

Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls. Full article

In order to produce detergents with improved performance and good market acceptability, it is crucial to develop formulations with improved foamability and cleaning performance. The use of a delicate balance of surfactants and additives is an appealing strategy to obtain good results and enables a reduction in the amount of chemicals used in formulations. Mixtures of hydrophobically modified linear polymers and surfactants, as well as balanced mixtures with co-surfactants and/or hydrotropes, are the most effective parameters to control foamability and foam stability. In the present study, the effect of the addition of hydrophobically modified linear polymers, nonionic co-surfactants and hydrotropes, and their mixtures to anionic and zwitterionic surfactant aqueous solutions was evaluated. It was found that the presence of the hydrophobically modified polymer (HM-P) prevented the bubbles from bursting, resulting in better stability of the foam formed using zwitterionic surfactant solutions. Also, the surfactant packing was inferred to be relevant to obtaining foamability. Mixtures of surfactants, in the presence of a co-surfactant or hydrotrope led, tendentially, to an increase in the critical packing parameter (CPP), resulting in higher foam volumes and lower surface tension for most of the studied systems. Additionally, it was observed that the good cleaning efficiency of the developed surfactant formulations obtained a higher level of fat solubilization compared to a widely used brand of commercial dishwashing detergent. Full article

An electrothermal analysis for barium-titanate-based ceramics is presented, combining the Heywang–Jonker model for the electric resistivity with a heat dissipation mechanism based on natural convection and radiation in a one-dimensional model on the device level with voltage as the control parameter. Both positive-temperature-coefficient (PTC) and negative temperature coefficient (NTC) effects are accounted for through the double Schottky barriers at the grain boundaries of the material. The problem formulated in this way admits uniform and non-uniform multiple-steady-state solutions that do not depend on the external circuit. The numerical bifurcation analysis reveals that the PTC effect gives rise to several multiplicites above the Curie point, whereas the NTC effect is responsible for the thermal runaway (temperature blowup). The thermal runaway phenomenon as a potential thermal shock could be among the possible reasons for the observed thermomechanical failures (delamination fracture). The theoretical results for the NTC regime and the thermal runaway are in agreement with the experimental flash sintering results obtained for barium titanate, and 3% and 8% yttria-stabilized zirconia. Full article

In real-world scenarios, we encounter non-exchangeable dependence structures. Our primary focus is on identifying and quantifying non-exchangeability in the tails of joint distributions. The findings and methodologies presented in this study are particularly valuable for modeling bivariate dependence, especially in fields where understanding dependence patterns in the tails is crucial, such as quantitative finance, quantitative risk management, and econometrics. To grasp the intricate relationship between the strength of dependence and various types of margins, we explore three fundamental tail behavior patterns for univariate margins. Capitalizing on the probabilistic features of tail non-exchangeability structures, we introduce graphical techniques and statistical tests designed for analyzing data that may manifest non-exchangeability in the joint tail. The effectiveness of the proposed approaches is illustrated through a simulation study and a practical example. Full article

Lindera benzoin is a dioecious understory shrub native to eastern North America. Northern spicebush is a beautiful shrub with a natural round shrub shape, golden-yellow fall foliage, attractive bright red drupes, and precocious yellow flowers in early spring; however, its market value as an ornamental value has been overlooked. To improve the ornamental values of this under-cultivated nursery crop, breeding for a better compact form, larger leaves, enlarged flower clusters and fruit, and increased stress tolerances could all be beneficial. Polyploidy manipulation is a valuable method to improve such traits for many ornamental plants. This study established the genome doubling method by oryzalin-infused solid agar treatment on young northern spicebush seedlings. The seedlings of two wild populations in North Carolina were collected and used. A total of 288 seedlings were treated with solid agar containing 150 µM oryzalin for 24, 72, and 120 h. The results were sporadic in their survival ratios and tetraploid conversion ratios between different treatments; however, a total of 16 tetraploid L. benzoin plants were produced in this study. The 24-h treatment showed the optimal result, with 7.1% of total treated seedlings or 15.2% of surviving seedlings converted into tetraploids. Tetraploid plants had visible differences in leaf morphology, a statistically significant enlarged stomata size, and reduced stomatal density compared to diploid plants. This research provides ploidy manipulation information for all future breeding processes of L. benzoin and related species. Full article