Growing dependence on fossil fuels is one of the critical factors accelerating climate change, a global concern that can destabilize ecosystems and economies worldwide. In this context, renewable energy is emerging as a sustainable and environmentally responsible alternative. Among the options, geothermal energy stands out for its ability to provide heat and electricity consistently and efficiently, offering a feasible solution to reduce the carbon footprint and promote more sustainable development in a globalized economy. In this work, a machine learning approach is proposed to predict the behavior of a horizontal heat exchanger from a bioclimatic house. First, a correlation analysis was conducted for optimal feature selection. Then, several regression techniques were applied to predict the output temperature of the geothermal exchanger. Satisfactory prediction results were obtained in different scenarios over the whole dataset. Also, a significant correlation between several sensors was concluded. Full article

The present study aimed to systematically investigate the underlying differences in flesh quality between wild and farmed Monopterus albus. Fifteen healthy M. albus per group with an average body weight of 45 g were sampled to analyze muscle parameters by biochemical indicators, histomorphology, and molecular biology. Compared with the wild fish, the farmed M. albus in flesh had lower crude protein, collagen, lysine, histidine, total amino acids, SFA, n-3 PUFA contents, and n-3/n-6 ratio (p < 0.05), and higher moisture, crude lipid, crude ash, MUFA, n-6PUFA, and total PUFA contents (p < 0.05). The thawing loss, drip loss, steaming loss, and boiling loss in the farmed group were significantly higher, and hardness, springiness, cohesiveness, gumminess, chewiness, and resilience were significantly lower than those in the wild group (p < 0.05). In addition, higher muscle fiber density and lower muscle fiber diameter were observed in wild M. albus (p < 0.05). In muscle transcriptome profiling, differentially expressed genes and enriched pathways are primarily associated with muscle development, protein synthesis, catabolism, lipid metabolism, and immunity. To the best of our knowledge, this is the first investigation that compares the flesh quality between wild and farmed M. albus in terms of biochemistry, histology, and molecular biology levels. Overall, wild M. albus had a higher nutritional value and texture quality than farmed M. albus. Full article

Tax policy is critical for business operations and export decisions. However, the relationship between tax sharing and export has been less frequently discussed. This paper explicitly examines the underexplored tax-sharing system’s effect on manufacturing exporters’ export quality and develops four hypotheses. We use data on Chinese manufacturing exporters and prefecture-level tax-sharing from 2008 to 2013 and employ an instrumental variable approach to alleviate the endogeneity problem. The empirical evidence supports our hypotheses. We find that an increase in the prefecture-level government tax-sharing ratio significantly reduces export product quality of firms. This quality effect can occur through channels, including tax burden effect, production scale effect, and innovation effect. Moreover, more productive firms and those operated in cities with stronger intellectual property protection can face a smaller quality-reducing effect. Our findings offer policy implications for improving China’s modernized tax system and trade upgrading. Policymakers should recalibrate the tax-sharing system to reduce the tax burden on manufacturing exporters, particularly for innovative and high-productivity firms, and bolster intellectual property rights to enhance export quality and support China’s trade and economic modernization. Full article

In the field of underwater acoustics, the signal-to-noise ratio (SNR) is generally low, and the underwater environment is complex and variable, making target azimuth estimation highly challenging. Traditional model-based subspace methods exhibit significant performance degradation when dealing with coherent sources, low SNR, and small snapshot data. To overcome these limitations, an improved model based on SubspaceNet, called PConv-GAM Residual SubspaceNet (PGR-SubspaceNet), is proposed. This model embeds the global attention mechanism (GAM) into residual blocks that fuse PConv convolution, making it possible to capture richer cross-channel and positional information. This enhancement helps the model learn signal features in complex underwater conditions. Simulation results demonstrate that the underwater target azimuth estimation method based on PGR-SubspaceNet exhibits lower root mean square periodic error (RMSPE) values when handling different numbers of narrowband coherent sources. Under low SNR and limited snapshot conditions, its RMSPE values are significantly better than those of traditional methods and SubspaceNet-based enhanced subspace methods. PGR-SubspaceNet extracts more features, further improving the accuracy of direction-of-arrival estimation. Preliminary experiments in a pool validate the effectiveness and feasibility of the underwater target azimuth estimation method based on PGR-SubspaceNet. Full article

Ambrosia beetles, particularly invasive species within the tribe Xyleborini, such as Xylosandrus germanus (Blandford, 1894), pose significant threats to various ecosystems and managed habitats worldwide. Monitoring these invaders is vital for effective pest management, typically accomplished through ethanol-baited traps. We compared trap efficacy using denatured ethanol versus absolute ethanol in orchards, tree nurseries, and lumber yards in northeastern Ohio, USA, finding that absolute ethanol traps captured significantly more X. germanus. Analysis revealed acetone, ethanol, and methyl isobutyl ketone in the denatured ethanol, likely impacting trap efficacy. Our study underscores the importance of using pure denatured ethanol without acetone for effective monitoring, especially for X. germanus. Exotic xyleborines dominated trap captures across various habitats, emphasizing the need for tailored pest management strategies. Further research is warranted to explore the chemical ecology of ambrosia beetles and the influence of ethanol impurities on trap effectiveness. Full article

An efficient and reliable management system for a cluster of distributed energy resources (DERs) is essential for the sustainable and cost-effective peak management (PM) operation of the power grid. The virtual power plant (VPP) provides an efficient way to manage a variety of DERs for the PM process. This paper proposes a VPP framework for PM of local distribution companies by optimizing the self-scheduling of available resources, considering uncertainties and constraints. The study examines two separate scenarios and introduces novel algorithms for determining threshold values in each scenario. An approach is suggested for the transaction between VPP and the aggregator models. The proposed technique intends to determine the optimal amount of capacity that aggregators can allocate for the day-ahead PM procedure while accounting for both thermostatically controlled and non-thermostatically controlled loads. The proposed VPP framework shows promising results for reducing demand charges and optimizing energy resources for PM. Full article

The velocity field and temperature field are crucial for metal foams to be used as a heat exchanger, but they are difficult to obtain through physical experiments. In this work, the fluid flow behavior and heat transfer performance in open-cell metal foam were numerically studied. Porous 3D models with different porosities (55–75%) and pore sizes (250 μm, 550 μm, and 1000 μm) were created based on the porous structure manufactured by the Lost Carbonate Sintering method. A wide flow velocity range from 0.0001 m/s to 0.3 m/s, covering both laminar and turbulent flow regimes, is fully studied for the first time. Pressure drop, heat transfer coefficient, permeability, form drag coefficient, temperature and velocity distributions were calculated. The calculated results agree well with our previous experimental results, indicating that the model works well. The results showed that pressure drop increased with decreasing porosity and increasing pore size. Permeability increased and the form drag coefficient decreased with increasing porosity, and both increased with increasing pore size. The heat transfer coefficient increased with increasing velocity and porosity, whereas it slightly decreased with increasing pore size. The results also showed that at high velocity, only the metal foam close to the heat source contributes to heat dissipation. Full article

Background and Objective: Colorectal cancer (CRC) is among the most common types of cancer. Although the disease is treatable in its early stages, five-year survival falls below 20% in the later stages. CEA and CA19-9 are tumor markers used in the diagnosis and follow-up of the disease in clinical practice; however, their diagnostic effectiveness is insufficient. Therefore, the identification of biomarkers that can be easily studied from serum and can diagnose CRC and determine its severity is highly important. In this context, dickkopf1 (DKK1) and cytoskeleton-associated protein 4 (CKAP4) are both promising biomarkers. Materials and Methods: Serum DKK1 and CKAP4 levels were measured in 55 patients with CRC and 40 healthy controls. The patients with CRC were divided into groups based on pathological stages and histological differentiation. The serum levels of both proteins in patients with CRC were measured preoperatively and 10 and 30 days postoperatively. Results: Serum DKK1 and CKAP4 were significantly higher in the CRC group than in the healthy controls (p < 0.05). Serum levels of both proteins rose in line with the disease stage and grade but decreased following surgical resection. A positive correlation was observed between tumor diameter and protein blood levels. The diagnostic efficacy of DKK1 and CKAP4 in CRC (approximately 95%) was higher than that of markers such as CEA and CA19-9. Conclusions: The DKK1 and CKAP4 serum values of patients with CRC are promising biomarkers. They can potentially be used in CRC management, namely, in the diagnosis and treatment of tumor response access and in tumor aggressiveness prediction. Full article

Kidney transplant recipients are at an increased risk of hospitalisation and death from SARS-CoV-2 infection, and standard two-dose vaccination schedules are typically inadequate to generate protective immunity. Gut dysbiosis, which is common among kidney transplant recipients and known to effect systemic immunity, may be a contributing factor to a lack of vaccine immunogenicity in this at-risk cohort. The gut microbiota modulates vaccine responses, with the production of immunomodulatory short-chain fatty acids by bacteria such as Bifidobacterium associated with heightened vaccine responses in both observational and experimental studies. As SCFA-producing populations in the gut microbiota are enhanced by diets rich in non-digestible fibre, dietary supplementation with prebiotic fibre emerges as a potential adjuvant strategy to correct dysbiosis and improve vaccine-induced immunity. In a randomised, double-bind, placebo-controlled trial of 72 kidney transplant recipients, we found dietary supplementation with prebiotic inulin for 4 weeks before and after a third SARS-CoV2 mRNA vaccine to be feasible, tolerable, and safe. Inulin supplementation resulted in an increase in gut Bifidobacterium, as determined by 16S RNA sequencing, but did not increase in vitro neutralisation of live SARS-CoV-2 virus at 4 weeks following a third vaccination. Dietary fibre supplementation is a feasible strategy with the potential to enhance vaccine-induced immunity and warrants further investigation. Full article

When discussing “wartime ephemera”, of the kind that has been passed down through families since the Second World War, Germany and Austria could be considered as a counterexample to Britain. In German and Austrian historical memory, “wartime” cannot be separated from pre-war Nazi society (beginning in 1933 and 1938, respectively). Moreover, what we might loosely call the “Antiques Roadshow experience”—discussing family objects from the Second World War in a sympathetic public forum—has never been open to the majority of Germans and Austrians, who were rather inclined to hide and forget such objects in the family home. Even so, mundane Nazi-era objects survived in their millions and this essay discusses their display in German and Austrian history museums. There, they serve to illustrate a history of mentalities during and after the Nazi regime. Austrian museums are currently playing a proactive role in the transfer of objects from the private, family archive to public, cultural memory. This article considers how notions of “family” are constructed in museum discourse and asks how the millions of German and Austrian citizens and residents with no family connection to the Second World War can be included in a form of national family storytelling that arguably “re-ethnicizes” memory. Full article

With regard to the assertion of the nature of the world, primitive Buddhism advocates “all phenomena that arise from causes” and opposes the existence of “God” or “Creator”, who created everything in the universe, which is significantly different from monotheistic beliefs such as Brahmanism, Christianity, and Islam and is therefore often called “atheism”. This paper introduces the Buddhist cosmology of Mount Sumeru and the tri-sahasra mahā-sahasra lokadhātu under the perspective of comparative religions and the first human beings who came to this world from the ābhāsvara-deva as recorded in the Buddhist scriptures and explores the question of whether Buddhism is atheistic. It is believed that the key to the debate between Chinese and Western scholars on whether Buddhism is atheistic is the difference in understanding the concept of “God”. Buddhism does not deny the supernatural power of “ghosts and gods”, so its essence is still theism. Full article

This study investigated the influence of microstructure on the performance of Ag inkjet-printed, resistive temperature detectors (RTDs) fabricated using particle-free inks based on a silver nitrate (AgNO₃) precursor and ethylene glycol as the ink solvent. Specifically, the temperature coefficient of resistance (TCR) and sensitivity for sensors printed using inks that use monoethylene glycol (mono-EG), diethylene glycol (di-EG), and triethylene glycol (tri-EG) and subjected to a low-pressure argon (Ar) plasma after printing were investigated. Scanning electron microscopy (SEM) confirmed previous findings that microstructure is strongly influenced by the ink solvent, with mono-EG inks producing dense structures, while di- and tri-EG inks produce porous structures, with tri-EG inks yielding the most porous structures. RTD testing revealed that sensors printed using mono-EG ink exhibited the highest TCR (1.7 × 10⁻³/°C), followed by di-EG ink (8.2 × 10⁻⁴/°C) and tri-EG ink (7.2 × 10⁻⁴/°C). These findings indicate that porosity exhibits a strong negative influence on TCR. Sensitivity was not strongly influenced by microstructure but rather by the resistance of RTD. The highest sensitivity (0.84 Ω/°C) was observed for an RTD printed using mono-EG ink but not under plasma exposure conditions that yield the highest TCR. Full article

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors. Full article

A decoupled model predictive control (MPC) power balancing strategy for a coupled inductor-based flying capacitor DC–DC converter (FCDC) is a proposed to solve the power imbalance caused by the parameter differences in the coupled inductor. The decoupled mathematical model of coupled inductor FCDC is firstly derived by analyzing the converter operation state under various modes. On this basis, the control relationship between inductor current and flying capacitor (FC) voltage is redefined and an MPC power balance strategy based on the inductor current with single-step optimization is proposed. The proposed MPC strategy not only achieves decoupled power balancing control but also solves multi-objective dynamic optimization control of the inductor current and FC voltage, greatly reducing the computation load. A detailed theoretical analysis of the proposed strategy is presented and the balancing performance is effectively verified through the experiments. Full article

Selenium (Se) is an essential trace element for human physiological metabolism. The application of organic Se as a source to cultivate Se-rich plants for micronutrient supplementation has been receiving increasing attention. In our study, a bacterial strain named H1 was isolated from the soil in Heilongjiang Province, China, and under optimal culture conditions, the unit Se content could reach 3000 μg·g⁻¹ and its 16S ribosomal DNA sequence seemed to be a new molecular record of an Enterobacter species. After the domestication of Se tolerance and Se-rich experiments, H1 can be used as a Se source for cultivation of Se-rich Auricularia auricula. The results showed that soluble protein, soluble sugar, free amino acid and vitamin C contents in Auricularia auricula were notably increased by 28.7%, 21.8%, 32.5% and 39.2% under the treatment of Se concentration of 0.24 mg·kg⁻¹, respectively. These findings enhance our understanding that H1 is more conducive to Se uptake and nutrient accumulation. Full article

This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field. Full article

The origin of nuclear magnetic shielding in diamagnetic molecules is discussed, pointing out various contributions to the shielding from electrons and the effects of intra- and intermolecular interactions. In NMR practice, chemical shifts are determined first as the measure of shielding in observed samples. The descriptions of shielding and chemical shifts are not fully consistent. Gas phase studies permit the withdrawal of intermolecular contributions from shielding and obtaining the magnetic shielding data in isolated molecules. The shielding determination in molecules is possible using at least three methods delivering the reference shielding standards for selected nuclei. The known shielding of one magnetic nucleus can be transferred to other nuclei if the appropriate nuclear magnetic moments are available with satisfactory accuracy. It is possible to determine the nuclear magnetic dipole moments using the most advanced ab initio shielding calculations jointly with the NMR frequencies measurements for small-sized isolated molecules. Helium-3 gas is postulated as all the molecules’ primary and universal reference standard of shielding. It can be easily applied using common deuterium lock solvents as the secondary reference standards. The measurements of absolute shielding are available for everyone with the use of standard NMR spectrometers. Full article

With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed. Full article

The global construction industry significantly contributes to energy consumption and greenhouse gas emissions, necessitating immediate action for sustainable development. Recognizing the impact of buildings on emissions, the United Nations has set ambitious energy-related goals for 2030. Retrofitting buildings emerges as a strategic method for reducing energy consumption, offering lower environmental impact and life cycle costs. However, retrofitting is a complex process influenced by diverse factors such as policies, available resources, techniques, building-specific data, and uncertainties. Thus, this paper reviews the existing literature on retrofitting strategies for tropical and humid climates to identify effective approaches for enhancing energy efficiency, thermal comfort, and overall building performance in these regions. Through comprehensive analyses, including bibliometric analysis using VOSviewer version 1.6.18 and systematic assessments, this study investigates various retrofitting strategies. This study categorizes tropical climates into Af (Tropical Rainforest Climate) and Aw (Tropical Savanna Climate) based on the Köppen climate classification. It reveals distinct emphases, with Af climates concentrating on office buildings and Aw climates prioritizing residential structures. Passive strategies were predominantly favored in office buildings, with glazing being the most commonly implemented approach. Residential structures, on the other hand, adopted a combination of passive strategies such as phase change materials along with active methods like appliance replacement. Educational buildings tended to rely on passive strategies, including roof covers, shading, and glazing. The absence of specific cost values underscores the importance of establishing baseline metrics, revealing significant challenges in retrofit techniques. This study further highlights an opportunity to explore passive methods in educational buildings, stressing the need for comprehensive guidelines, especially in institutional settings. Moreover, it emphasizes the urgency for ambitious regulations to address carbon emissions and optimize energy efficiency in tropical climates. Full article

With the aim of solving the problem of identifying complex underwater acoustic targets using a single signal feature in the Time–Frequency(TF) feature, this paper designs a method that recognizes the underwater targets based on the Delay-Doppler joint feature. First, this method uses symplectic finite Fourier transform (SFFT) to extract the Delay-Doppler features of underwater acoustic signals, analyzes the Time–Frequency features at the same time, and combines the Delay-Doppler (DD) feature and Time–Frequency feature to form a joint feature (TF-DD). This paper uses three types of convolutional neural networks to verify that TF-DD can effectively improve the accuracy of target recognition. Secondly, this paper designs an object recognition model (TF-DD-CNN) based on joint features as input, which simplifies the neural network’s overall structure and improves the model’s training efficiency. This research employs ship-radiated noise to validate the efficacy of TF-DD-CNN for target identification. The results demonstrate that the combined characteristic and the TF-DD-CNN model introduced in this study can proficiently detect ships, and the model notably enhances the precision of detection. Full article

Developing materials with dynamic room-temperature phosphorescence (RTP) properties is crucial for expanding the applications of organic light-emitting materials. In this study, we designed and synthesized two novel RTP molecules by combining functional units, incorporating the folded unit thianthrene into the classic luminescent cores thioxanthone or anthraquinone to construct TASO and TA2O. In this combination, the TA unit contributes to the enhancement of spin–orbit coupling (SOC), while the luminescent core governs the triplet energy level. After the strategic manipulation of SOC using the thianthrene unit, the target molecules exhibited a remarkable enhancement in RTP performance. This strategy led to the successful development of TASO and TA2O molecules with outstanding dynamic RTP properties when exposed to continuous ultraviolet irradiation, a result that can be ascribed to their efficient RTP, improved absorption ability, and oxygen-sensitive RTP properties. Leveraging the oxygen-mediated ultraviolet-radiation-induced RTP enhancement in TASO-doped polymer films, we developed a novel time-resolved detection technique for identifying phase separation in polymers with varying oxygen permeability. This research offers a promising approach for constructing materials with dynamic RTP properties. Full article