Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R²), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures. Full article

Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead to mental health issues due to repeated exposure to violence. Managing health and welfare issues becomes pivotal in group-housed animal settings, where individual care is challenging on large farms with limited staff. The necessity for computer vision systems to analyze sow behavior and detect deviations indicative of health problems is apparent. Beyond observing changes in behavior and physical traits, computer vision can accurately detect estrus based on vulva characteristics and analyze thermal imagery for temperature changes, which are crucial indicators of estrus. By automating estrus detection, farms can significantly enhance breeding efficiency, ensuring optimal timing for insemination. These systems work continuously, promptly alerting staff to anomalies for early intervention. In this research, we propose part of the solution by utilizing an image segmentation model to localize the vulva. We created our technique to identify vulvae on pig farms using infrared imagery. To accomplish this, we initially isolate the vulva region by enclosing it within a red rectangle and then generate vulva masks by applying a threshold to the red area. The system is trained using U-Net semantic segmentation, where the input for the system consists of grayscale images and their corresponding masks. We utilize U-Net semantic segmentation to find the vulva in the input image, making it lightweight, simple, and robust enough to be tested on many images. To evaluate the performance of our model, we employ the intersection over union (IOU) metric, which is a suitable indicator for determining the model’s robustness. For the segmentation model, a prediction is generally considered ‘good’ when the intersection over union score surpasses 0.5. Our model achieved this criterion with a score of 0.58, surpassing the scores of alternative methods such as the SVM with Gabor (0.515) and YOLOv3 (0.52). Full article

Acrodermatitis chronica atrophicans (ACA) is not an infrequent condition in Europe. However, the characteristic skin lesions are often confused by non-dermatologists with other conditions. We report three unusual cases in which we made a definitive diagnosis of ACA complicated by cutaneous marginal zone lymphoma, juxta-articular fibrotic nodules, or bilateral sensory polyneuropathy. In all cases, correct diagnosis and adequate treatment was delayed over a period of at least 12 months. We initiated systemic antibiotics resulting in full recovery in these patients. The present case reports underscore that ACA may be associated with unusual clinical presentation which potentially result in delay of correct diagnosis and treatment. Hence, ACA diagnosis may be considerably delayed leading to inappropriate therapy exposure, prolonged patients’ suffering, and causing unnecessary cost. Thus, physicians who are not familiar with skin conditions should seek a timely consultation a dermatologist. Full article

With increased worldwide energy demand and carbon dioxide emissions from the use of fossil fuels, severe problems are being experienced in modern times. Energy is one of the most important resources for humankind, and its needs have been drastically increasing due to energy consumption, the rapid depletion of fossil fuels, and environmental crises. Therefore, it is important to identify and search for an alternative to fossil fuels that provides energy in a reliable, constant, and sustainable way that could use available energy sources efficiently for alternative renewable sources of fuel that are clean, non-toxic, and eco-friendly. In this way, there is a dire need to develop technologies for biofuel production with a focus on economic feasibility, sustainability, and renewability. Several technologies, such as biological and thermochemical approaches, are derived from abundant renewable biological sources, such as biomass and agricultural waste, using advanced conversion technologies for biofuel production. Biofuels are non-toxic, biodegradable, and recognized as an important sustainable greener energy source to conventional fossil fuels with lower carbon emissions, combat air pollution, empower rural communities, and increase economic growth and energy supply. The purpose of this review is to explain the basic aspects of biofuels and their sustainability criteria, with a particular focus on conversion technologies for biofuel production, challenges, and future perspectives. Full article

Long-duration energy storage is the key challenge facing renewable energy transition in the future of well over 50% and up to 75% of primary energy supply with intermittent solar and wind electricity, while up to 25% would come from biomass, which requires traditional type storage. To this end, chemical energy storage at grid scale in the form of fuel appears to be the ideal option for wind and solar power. Renewable hydrogen is a much-considered fuel along with ammonia. However, these fuels are not only difficult to transport over long distances, but they would also require totally new and prohibitively expensive infrastructure. On the other hand, the existing natural gas pipeline infrastructure in developed economies can not only transmit a mixture of methane with up to 20% hydrogen without modification, but it also has more than adequate long-duration storage capacity. This is confirmed by analyzing the energy economies of the USA and Germany, both possessing well-developed natural gas transmission and storage systems. It is envisioned that renewable methane will be produced via well-established biological and/or chemical processes reacting green hydrogen with carbon dioxide, the latter to be separated ideally from biogas generated via the biological conversion of biomass to biomethane. At the point of utilization of the methane to generate power and a variety of chemicals, the released carbon dioxide would be also sequestered. An essentially net zero carbon energy system would be then become operational. The current conversion efficiency of power to hydrogen/methane to power on the order of 40% would limit the penetration of wind and solar power. Conversion efficiencies of over 75% can be attained with the on-going commercialization of solid oxide electrolysis and fuel cells for up to 75% penetration of intermittent renewable power. The proposed hydrogen/methane system would then be widely adopted because it is practical, affordable, and sustainable. Full article

Considering the rapid evolution of lithium silicate-based glass ceramics (LSCs) in dentistry, this review paper aims to present an updated overview of the recently introduced commercial novel LSCs. The clinical and in vitro English-language literature relating to the microstructure, manufacturing, strengthening, properties, surface treatments and clinical performance of LSC materials was obtained through an electronic search. Findings from relevant articles were extracted and summarised for this manuscript. There is considerable evidence supporting the mechanical and aesthetic competency of LSC variants, namely zirconia-reinforced lithium silicates and lithium–aluminium disilicates. Nonetheless, the literature assessing the biocompatibility and cytotoxicity of novel LSCs is scarce. An exploration of the chemical, mechanical and chemo-mechanical intaglio surface treatments—alternative to hydrofluoric acid etching—revealed promising adhesion performance for acid neutralisation and plasma treatment. The subtractive manufacturing methods of partially crystallised and fully crystallised LSC blocks and the additive manufacturing modalities pertaining to the fabrication of LSC dental restorations are addressed, wherein that challenges that could be encountered upon implementing novel additive manufacturing approaches using LSC print materials are highlighted. Furthermore, the short-term clinical performance of zirconia-reinforced lithium silicates and lithium–aluminium disilicates is demonstrated to be comparable to that of lithium disilicate ceramics and reveals promising potential for their long-term clinical performance. Full article

Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia’s weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes. Full article

The rapid advancement of communication technologies underscores the urgent need for robust and adaptable emergency communication systems (ECSs), particularly crucial during crises and natural disasters. Although network-based ECSs have been extensively studied, integrating open-source technologies, such as software-defined wide area networks (SD-WAN) with private long-term evolution (LTE) base stations, is a relatively unexplored domain. This study endeavors to fill this gap by introducing an experimental ECS platform that utilizes a hybrid network, incorporating a VoIP network to enhance open-source and on-premises communications in targeted areas. Our hypothesis posits that a hybrid network architecture, combining SD-WAN and private LTE, can substantially improve the reliability and efficiency of ECSs. Our findings, supported by the open-source OMNeT++ simulator, illuminate the enhanced communication reliability of the network. Moreover, the proposed platform, characterized by autonomous wireless 4G/LTE base stations and an Asterisk VoIP server, demonstrates improved quality of service (QoS) and quality of experience (QoE), with minimal data loss. This research not only has immediate practical applications but also bears significant implications for the development of cost-effective, open-source communication networks, optimized for emergencies, critical infrastructure, and remote areas. Full article

Analyzing the isotopic composition of cosmic rays (CRs) provides valuable insights into the galactic environment and helps refine existing propagation models. A particular interest is devoted to secondary-to-primary ratios of light isotopic components of CRs, the measurement of which can provide complementary information with respect to secondary-to-primary ratios like B/C. Given the complexity of the concurrent measurement of velocity and momentum required to differentiate isotopes of the same Z, a task typically accomplished using magnetic spectrometers, existing measurements of these ratios only effectively characterize the low-energy region (below 1 GeV/nucl). This study introduces a novel technique for isotopic distinction in CRs at high energies up to 100 GeV/nucl based on multiple scattering, which, combined with the proposed measurement of velocity, represent an interesting alternative to magnetic spectrometers. The performance of this technique was assessed through a dedicated simulation using the GEANT4 package, with specific emphasis on Z = 1 isotopes. Full article

The effective action in the renormalizable quantum theory of gravity provides entropy because the total Hamiltonian vanishes. Since it is a renormalization group invariant that is constant in the process of cosmic evolution, we can show conservation of entropy, which is an ansatz in the standard cosmology. Here, we study renormalizable quantum gravity that exhibits conformal dominance at high energy beyond the Planck scale. The current entropy of the universe is derived by calculating the effective action under the scenario of quantum gravity inflation caused by its dynamics. We then argue that ghost modes must be unphysical but are necessary for the Hamiltonian to vanish and for entropy to exist in gravitational systems. Full article

The present research focuses on the effects of different processing routes on the physical and mechanical properties of nano Ti(CN)-based cermets with metallic binders. Tungsten carbide (WC) is added as a secondary carbide and Ni-Co is added as a metallic binder to nano Ti(CN)-based cermet processed via conventional and spark plasma sintering (SPS). A systematic comparison of the composition and sintering conditions for different cermets’ systems was carried out to design novel composition and sintering conditions. Nano TiCN powder was prepared by 30 h of ball milling. The highest density of >98.5% was achieved for the SPS-processed cermets sintered at 1200 °C and 1250 °C for 3 min at 60 MPa of pressure in comparison to the conventionally sintered cermets at 1400 °C for 1 h with a two-stage compaction process—uniaxially at 150 MPa and isostatically at 300 MPa of pressure. Comparative X-ray diffraction (XRD) analysis of the milled powders at different time intervals was performed to understand the characteristics of the as-received and milled powders. Peak broadening was observed after 5 h of ball milling, and it increased to 30 hr. Also, peak broadening and a refined carbide size was observed in the XRD and scanning electron microscope (SEM) micrographs of the SPS-processed cermet. Transmission electron microscope (TEM) analysis of the milled powder showed that its internal structure had a regular periodic arrangement of planes. SEM base scattered electron (BSE) images of all the cermets primarily showed three major microstructural phases of the core–rim–binder with black, grey, and white contrast, respectively. With the present sintering conditions, a high hardness of ~16 GPa and a fracture toughness of ~9 MPa m^1/2 were obtained for SPS-processed cermets sintered at higher temperatures. Full article

Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2–3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of $1\times {10}^{-2}$ when the channel estimation error is −30 dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2–3 dB to achieve a BER of $1\times {10}^{-3}$. Full article

Phenomenal socialism says that what we actually, directly, literally perceive is only or primarily instances of high-level phenomenal properties; this paper argues for phenomenal socialism in the weaker, primarily version. Phenomenal socialism is the philosophy of perception that goes with recognitionalism, which is the metaethics that goes with epiphanies. The first part states the recognitionalist manifesto. The second part situates this manifesto relative to some more global concerns, about naturalism, perception, the metaphysics of value, and theory vs. anti-theory in ethics. The third part rehearses two familiar views about the possible contents of perceptual experience, Phenomenal Conservativism and Phenomenal Liberalism. It notes that the usual catalogue omits two other theoretical possibilities, Phenomenal Socialism and Phenomenal Nihilism, and it defends a watered-down form of Phenomenal Socialism from four main objections. The fourth part makes some connections with the epistemology of modality and with the role of the imagination. Full article

The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy. Full article

In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining production safety and equipment reliability. This research introduces an improved bearing defect detection model, YOLOv8-LMG, which is based on the YOLOv8n framework and incorporates four innovative technologies: the VanillaNet backbone network, the Lion optimizer, the CFP-EVC module, and the Shape-IoU loss function. These enhancements significantly increase detection efficiency and accuracy. YOLOv8-LMG achieves a [email protected] of 86.5% and a [email protected]–0.95 of 57.0% on the test dataset, surpassing the original YOLOv8n model while maintaining low computational complexity. Experimental results reveal that the YOLOv8-LMG model boosts accuracy and efficiency in bearing defect detection, showcasing its significant potential and practical value in advancing industrial inspection technologies. Full article

Pesticides and fertilizers used in agriculture can negatively affect the soil, increasing its toxicity. In this work, a battery of whole-cell bacterial lux-biosensors based on the E. coli MG1655 strain with various inducible promoters, as well as the natural luminous Vibrio aquamarinus VKPM B-11245 strain, were used to assess the effects of agrochemical soil treatments. The advantages of using biosensors are sensitivity, specificity, low cost of analysis, and the ability to assess the total effect of toxicants on a living cell and the type of their toxic effect. Using the V. aquamarinus VKPM B-11245 strain, the synergistic effect of combined soil treatment with pesticides and mineral fertilizers was shown, which led to an increase in the overall (integral) toxicity of soils higher than that of the individual application of substances. Several probable implementation mechanisms of agrochemical toxic effects have been discovered. DNA damage caused by both SOS response induction and alkylation, oxidative stress due to increased superoxide levels, and damage to cellular proteins and membranes are among them. Thus, the usage of biosensors makes it possible to assess the cumulative effect of various toxicants on living organisms without using expensive chemical analyses. Full article

Novel therapies for the treatment of familial dilated cardiomyopathy (DCM) are lacking. Shaping research directions to clinical needs is critical. Triggers for the progression of the disorder commonly occur due to specific gene variants that affect the production of sarcomeric/cytoskeletal proteins. Generally, these variants cause a decrease in tension by the myofilaments, resulting in signaling abnormalities within the micro-environment, which over time result in structural and functional maladaptations, leading to heart failure (HF). Current concepts support the hypothesis that the mutant sarcomere proteins induce a causal depression in the tension-time integral (TTI) of linear preparations of cardiac muscle. However, molecular mechanisms underlying tension generation particularly concerning mutant proteins and their impact on sarcomere molecular signaling are currently controversial. Thus, there is a need for clarification as to how mutant proteins affect sarcomere molecular signaling in the etiology and progression of DCM. A main topic in this controversy is the control of the number of tension-generating myosin heads reacting with the thin filament. One line of investigation proposes that this number is determined by changes in the ratio of myosin heads in a sequestered super-relaxed state (SRX) or in a disordered relaxed state (DRX) poised for force generation upon the Ca²⁺ activation of the thin filament. Contrasting evidence from nanometer–micrometer-scale X-ray diffraction in intact trabeculae indicates that the SRX/DRX states may have a lesser role. Instead, the proposal is that myosin heads are in a basal OFF state in relaxation then transfer to an ON state through a mechano-sensing mechanism induced during early thin filament activation and increasing thick filament strain. Recent evidence about the modulation of these mechanisms by protein phosphorylation has also introduced a need for reconsidering the control of tension. We discuss these mechanisms that lead to different ideas related to how tension is disturbed by levels of mutant sarcomere proteins linked to the expression of gene variants in the complex landscape of DCM. Resolving the various mechanisms and incorporating them into a unified concept is crucial for gaining a comprehensive understanding of DCM. This deeper understanding is not only important for diagnosis and treatment strategies with small molecules, but also for understanding the reciprocal signaling processes that occur between cardiac myocytes and their micro-environment. By unraveling these complexities, we can pave the way for improved therapeutic interventions for managing DCM. Full article

Antibodies are protein molecules whose primary function is to recognize antigens. However, recent studies have demonstrated their ability to hydrolyze specific substrates, such as proteins, oligopeptides, and nucleic acids. In 2023, two separate teams of researchers demonstrated the proteolytic activity of natural plasma antibodies from COVID-19 convalescents. These antibodies were found to hydrolyze the S-protein and corresponding oligopeptides. Our study shows that for antibodies with affinity to recombinant structural proteins of the SARS-CoV-2: S-protein, its fragment RBD and N-protein can only hydrolyze the corresponding protein substrates and are not cross-reactive. By using strict criteria, we have confirmed that this proteolytic activity is an intrinsic property of antibodies and is not caused by impurities co-eluting with them. This discovery suggests that natural proteolytic antibodies that hydrolyze proteins of the SARS-CoV-2 virus may have a positive impact on disease pathogenesis. It is also possible for these antibodies to work in combination with other antibodies that bind specific epitopes to enhance the process of virus neutralization. Full article

Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence Analysis (CEPA), which is a nonparametric Bayesian approach suitable for understanding the precedence relationships in censored multivariate events. CEPA aims to analyze the precedence relationships between events to predict subsequent occurrences effectively. We applied CEPA to neonatal data from the National Health Insurance Service, identifying the precedence relationships among the seven most commonly diagnosed diseases categorized by the International Classification of Diseases. This analysis revealed a typical diagnostic sequence, starting with respiratory diseases, followed by skin, infectious, digestive, ear, eye, and injury-related diseases. Furthermore, simulation studies were conducted to demonstrate CEPA suitability for censored multivariate datasets compared to traditional models. The performance accuracy reached 76% for uniform distribution and 65% for exponential distribution, showing superior performance in all four tested environments. Therefore, the statistical approach based on CEPA enhances our understanding of disease interrelationships beyond competitive methodologies. By identifying disease precedence with CEPA, we can preempt subsequent disease occurrences and propose a healthcare system based on these relationships. Full article

The introduction of high-cost medications often poses challenges in achieving cost-effectiveness for drug insurance coverage. Incorporating future price reductions for these medications may enhance their cost-effectiveness. We examined the influence of future cost reductions mandated by the national insurer’s equal pricing for equivalent drugs (EPED) policy on the cost-effectiveness of dupilumab, a biologic drug for moderate to severe atopic dermatitis in the Korean healthcare system. We conducted a policy simulation study using semi-Markovian cost utility analysis of dupilumab in combination with supportive care (SC) versus SC alone, with and without the EPED policy adjustment. The EPED would lower dupilumab’s price to 70% following the entry of a biosimilar drug in 10.3 years. Scenario analyses quantified the impact of changing time to the EPED, chemical versus biological designation, response criteria, discount rates, and time horizons on the Incremental Cost-Effectiveness Ratio (ICER) and acceptability with and without EPED adjustment. The EPED adjustment of dupilumab’s future price significantly improved its cost-effectiveness, with a 9.7% decrease in ICER and a substantial 14.6% increase in acceptability. Assuming EPED in 5 years, the ICER fell below the predefined willingness-to-pay threshold. If dupilumab were a chemical drug, EPED adjustment demonstrated a 19.1% increase in acceptability. Incorporating future cost reductions via the EPED system in economic evaluations is crucial, especially for drugs facing imminent generic entry. This study underscores the importance of EPED adjustment in the cost-effectiveness analysis of innovative medications, especially for those nearing willingness-to-pay thresholds. Full article