Search Results (91,984)

Sensitivity and effective sensing range are core performance metrics of flexible pressure sensors, directly dictating their practical applicability. A key challenge in sensor design is sensitivity degradation with elevated pressure, hindering synergistic optimization of high sensitivity and broad sensing range, while cumbersome electrode fabrication further impedes facile preparation and large-scale deployment of high-performance devices. Herein, this work proposes a novel fabrication strategy for flexible iontronic pressure sensors via direct laser writing (DLW) technology. A controllable ultraviolet laser patterns polyimide substrates to fabricate hierarchical stepped conoid-like microstructural templates, which are transferred to ion gels through reverse molding. The DLW-enabled precise geometric control and hierarchical conical architectures efficiently amplify interfacial contact area variation under pressure, significantly boosting sensitivity. The resultant sensor achieves a high sensitivity of 118.4 kPa⁻¹ and a broad detection range up to 2000 kPa, with fast response/recovery times of 38.4 ms and 47 ms and excellent mechanical stability enduring 2000 loading–unloading cycles at 850 kPa. Multi-scenario physiological signal monitoring validates its accurate capture of laryngeal vibrations and joint movements. This work establishes a straightforward, efficient microfabrication route for high-performance flexible iontronic sensors, accelerating their practical application in wearable health monitoring and related fields. Full article

Wood finger joints are widely used in both structural timber and high-quality furniture due to their ability to create long, continuous members from shorter pieces. The mechanical performance of these joints depends not only on the wood species but also on the geometry of the interlocking teeth and the quality of the adhesive bond. This study explores how the geometry of finger joints affects the tensile behavior and fracture characteristics of beech (Fagus sylvatica L.) and oak (Quercus robur L.). Specimens with varying tooth dimensions were tested using a 50 kN universal testing machine from Shimadzu. Key metrics such as ultimate tensile load, effective cross-sectional area, cohesive stress, energy required to cause failure, and fracture energy (Gc) at 0.5, 1.0, and 2.0 mm displacements were systematically measured. The results revealed that beech specimens achieved ultimate tensile loads up to 21,320 N and cohesive stress of 204 MPa, while oak reached 21,631 N with a cohesive stress of 239 MPa. Fracture energy (G_c) values ranged from 0.036 N/mm for beech to 0.051 N/mm for oak, depending on joint geometry. Results show that both the type of wood and the tooth design, including width and length, play a decisive role in joint performance. In general, longer teeth and larger bonded areas improved tensile capacity and increased resistance to fracture. These findings offer deeper insights into the fracture mechanics of hardwood finger joints and provide practical guidance for optimizing glued connections in furniture and structural timber. The collected data can also support accurate modeling, quality assurance, and numerical simulations in future studies. Full article

Industrial symbiosis (IS) research has documented many successful ecosystems but still lacks an empirically grounded typology linking resource flow configurations to environmental outcomes across diverse contexts. This study develops such a typology and tests whether distinct configurations achieve comparable environmental performance through different pathways—the configurational principle of equifinality. Drawing on 68 documented IS ecosystems across 48 countries, we apply k-means clustering to five flow-intensity dimensions—material, energy, water, logistics, and knowledge—and characterise the resulting partition using one-way ANOVA, Tukey HSD post hoc tests, multinomial logistic regression, and a Cox proportional-hazards model. Four configurations emerge: a dominant low-flow group (n = 34) and three coordinated configurations—energy–knowledge (n = 11), material-dominant (n = 16), and water-oriented (n = 7). The three coordinated configurations all significantly outperform the low-flow group on environmental performance (F(3, 57) = 11.60, p < 0.001), with effect sizes very similar and no significant differences among them, providing direct empirical evidence for equifinality. Economic performance does not differ significantly across configurations, and the multinomial model of contextual predictors is jointly insignificant—a pattern we read as consistent with equifinal contextual pathways rather than as a methodological flaw. Robustness checks across alternative clustering algorithms, operationalisations, and sub-samples support the typology’s stability. This study contributes an empirically grounded framework for circular economy practice that moves beyond one-size-fits-all prescriptions and offers a configurational lens for the design of sustainable industrial ecosystems. Full article

Chikungunya virus (CHIKV) pathogenesis research has long been constrained by the lack of suitable immunocompetent rodent models. Through serial passaging in A129 and C57BL/6 mice, we obtained an adapted strain (CHIKV-Adapt) harboring an E2-K200R substitution along with non-structural protein mutations. Phenotypic analysis in C57BL/6 mice, BALB/c mice, and hamster models demonstrated that compared to the wild-type virus CHIKV-Adapt induced significantly higher and more prolonged viremia, broader tissue tropism, and more severe internal joint inflammation, without exacerbating external swelling. Notably, the K200R mutation did not alter the viral replication kinetics in vitro and was predicted not to affect its binding pattern to the MXRA8 receptor. Furthermore, mice challenged 160 days after primary infection exhibited nearly complete protective immunity. These findings indicate that E2-K200R is a critical adaptive mutation that, together with accompanying non-structural mutations, significantly enhances CHIKV replication capacity and pathogenicity in immunocompetent rodents without changing its in vitro replication ability or predicted receptor-binding mode. The acquisition of this adapted strain provides a new tool for CHIKV pathogenesis research and vaccine evaluation. Full article

Sonar is a fundamental tool for underwater target detection. However, conventional detection systems often suffer from poor sensor consistency and high fabrication costs. More critically, for low-frequency operation, the required array aperture becomes prohibitively large, limiting their deployment on small, mobile underwater platforms. To address the demand for compact, high-performance sensing solutions, this paper presents a miniaturized Micro-electromechanical Systems (MEMS) hydrophone array designed for underwater target detection. The array consists of six elements with a spacing of 0.25 m. Each element is approximately 22 mm in diameter and encapsulated in polyurethane via a casting and curing process. The core sensing element, a MEMS acoustic pressure hydrophone, exhibits a sensitivity of −177.2 ± 1.5 dB (re: 1 V/µPa) across the 20 Hz to 4 kHz frequency range and a noise resolution of approximately 59.5 dB (re: 1 µPa/√Hz) at 1 kHz. A key challenge in array-based detection is the phase mismatch among acquisition channels, which degrades algorithm performance. To mitigate this, we propose a phase self-correction method based on interleaved ADC acquisition control, enabling synchronous multi-channel sampling and effectively eliminating system-level phase errors. Furthermore, to overcome the inherent aperture limitations of conventional beamforming (CBF) applied to a miniaturized array, a differential beamforming (DBF) algorithm is adopted. This approach is less frequency-dependent and can approximate a frequency-invariant beam pattern, making it well-suited for miniaturized arrays. Simulation results confirm the theoretical validity of the DBF algorithm for the proposed MEMS hydrophone array. Sea trial data further demonstrate that this method achieves higher target detection accuracy compared to CBF techniques. Full article

Polyhalite, K₂SO₄•MgSO₄•2CaSO₄•2H₂O, a ternary evaporite mineral, is commonly found in evaporitic rock salt strata, where it acts as an indicator mineral for potash evaporite deposits. As a directly exploitable mineral potash fertilizer, polyhalite serves as an important substitute for potassium resources. The thermodynamic properties of polyhalite remain poorly characterized experimentally; consequently, current estimates predominantly rely on predictive modeling and indirect experimental approaches. The Raman spectra of free SO₄²⁻ vibrational modes in various sulfate minerals are sensitive to the local symmetry and hydrogen-bonding environment within crystal hydrates, and are directly influenced by the surrounding crystal field. This sensitivity makes Raman spectroscopy a powerful tool for investigating and identifying the crystal structures of sulfate minerals. In this work, the thermodynamic and Raman vibrational properties of polyhalite were investigated using density functional theory (DFT). Phonon calculations at the optimized geometry were employed to compute polyhalite’s key thermodynamic properties—specific heat, entropy, enthalpy, Gibbs free energy, and Debye temperature—over a temperature range of 0–1000 K. The results showed that: (1) the computed volume exhibited minimal error, approximately 0.87%, compared to experimental data; (2) the calculated values for the isobaric heat capacity and entropy were 420.72 and 531.39 J·mol⁻¹·K⁻¹ at 298.15 K, respectively; and (3) the calculated value for the free energy of formation at 298.15 K was −5670 kJ·mol⁻¹. The computed Raman spectrum results showed that the typical spectral features of polyhalite are: (1) ν1 for 1024 cm⁻¹, symmetric stretching mode; (2) ν2 for 464 cm⁻¹, symmetry bending mode; and (3) ν4 for 627 cm⁻¹, anti-symmetry bending mode. Full article

With the growing adoption of data-driven workflows and the need to compare numerous interior design alternatives in housing renewal, scalable and consistent assessment of interior space quality is increasingly important; however, current practice still depends on manual scoring and expert judgment. To address this gap, we propose an automation-ready framework that evaluates interior space quality from visual data. We construct the Functionality–Healthiness–Aesthetics Spatial Interior Dataset-10K (FHASID-10K) with 13,962 images for systematic validation. Three sub-models quantify functionality via space utilization and circulation smoothness, healthiness via detection of health-related visual elements, and aesthetics via semantic visual representations with regression-based prediction. Dimension scores are standardized and fused using the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) to produce a comprehensive score for ranking and grading. Experiments show stable score distributions and clear differentiation across space categories and style–space combinations. A gradient-boosted decision tree (GBDT) surrogate reconstructs the fused score with high accuracy (test R² = 0.9992; MSE = 1.1 × 10⁻⁵), and human-subject evaluation shows strong agreement with overall-quality ratings (r = 0.760, p < 0.001). Overall, the framework enables scalable benchmarking, scheme comparison, and decision support. Full article

Oligodendroglial cells are the myelinating glial cells of the central nervous system (CNS), and their morphological differentiation is a prerequisite for efficient myelin formation, which is essential for proper neuronal function. While oligodendroglial morphological changes normally proceed through tightly regulated developmental transitions, disruption of the underlying molecular mechanisms can lead to aberrant cellular phenotypes characterized by either premature, insufficient, or excessive differentiation. Although the phosphatidylinositol 3-kinase (PI3K) and its downstream Akt kinase signaling are well established as major drivers of oligodendrocyte morphological differentiation, myelination, and CNS white matter formation, how its negative regulator, phosphatase and tensin homolog (PTEN), is involved in the regulation of oligodendroglial morphogenesis remains incompletely understood. Recent genetic studies have highlighted a spectrum of disorders caused by PTEN dysfunction, conceptually established but currently evolving as PTENopathy, which has been partially associated with white matter abnormalities. Here, we report that, in an experimental model using the FBD-102b cell line, a well-established model of oligodendroglial cell differentiation, chemical inhibition of PTEN enhances pronounced morphological changes characterized by widespread membranes, accompanied by increased expression of differentiation and/or myelin marker proteins. We then focused on Rho family small GTPases, central regulators of cell morphogenesis, and examined their potential involvement downstream of this signaling. Expression of the RhoG-binding domain (RBD) of engulfment and cell motility 1 (ELMO1) attenuated the increased morphological changes. Similarly, inhibition of downstream Akt signaling also reversed these changes. Taken together, these results provide insight into how balanced regulation between PTEN and downstream signaling molecules governs oligodendroglial cell differentiation and suggest that dysregulation of this signaling equilibrium may contribute to cellular phenotypes relevant to disease-associated cellular alterations. Full article

Background: Prostate cancer (PCa) arises from complex interactions among host genetics, androgen signaling, and microbial communities. Emerging genomic evidence supports the presence of microbial consortia within prostate tissue, suggesting that microbial genes, metabolites, and host–microbe interactions may contribute to chronic inflammation, oncogenic signaling, and therapeutic resistance. Methods: We conducted a narrative review using targeted searches of PubMed and Google Scholar for studies published between 2020 and 2025, complemented by selected mechanistic reports published in March 2026. Human studies and experimental research providing mechanistic insights into prostate models were prioritized. Due to the heterogeneous methodologies, evidence was synthesized qualitatively, with an emphasis on genomic and signaling perspectives. Results: Low-biomass microbial DNA is consistently detected in prostate tissue. Proteomic analyses of Corpora amylacea suggest a “fossil record” of past infections through sequestered microbial DNA and antimicrobial proteins, potentially priming tissue for long-term carcinogenic processes, although contamination remains a key limitation. Recurrent bacterial and viral signals, including Cutibacterium acnes, Escherichia coli, Pseudomonas, Acinetobacter, human papillomavirus, Epstein–Barr virus, and cytomegalovirus, appear to converge on a restricted set of tumor-relevant pathways, including TLR–NF-κB, MAPK, PI3K/AKT/mTOR, cGAS–STING, and p53/pRb disruption. These interactions may promote cytokine production, oxidative stress, DNA damage, epithelial–mesenchymal transition, extracellular matrix remodeling, immune evasion, and resistance to therapy. The gut–prostate axis further links intestinal dysbiosis and microbial metabolites with systemic IGF-1 signaling and castration resistance. Conclusions: Microbial genomic consortia in the prostate and gut may shape inflammatory, metabolic, and immune networks that influence PCa initiation and progression. However, most available data remain correlative and are limited by low-biomass sampling, contamination risk, and heterogeneous study designs. Future research should prioritize rigorous contamination control, longitudinal and prostate-specific mechanistic studies, and integrated multi-omic approaches to clarify causality and identify actionable microbial targets for prevention, diagnosis, and therapy. Full article

Cellular senescence represents a critical biological paradox in oncology. Although it evolved as a safety mechanism to halt tumorigenesis through stable cell cycle arrest, its persistence in tissues can alter the microenvironment, promoting tumor recurrence. In the context of glioblastoma (GBM), this phenomenon is critically important, as current standard therapies, such as radiotherapy and chemotherapy, inadvertently induce a state of senescence known as “therapy-induced senescence” (TIS). Senescent cells remain metabolically active and acquire a unique Senescence-Associated Secretory Phenotype (SASP), characterized by the release of pro-inflammatory cytokines, proteases, and growth factors. SASP reshapes the tumor microenvironment (TME) through paracrine signals, promoting immunosuppression, invasiveness, drug resistance and tumor recurrence. Different glial populations, including astrocytes, microglia, and oligodendrocyte precursor cells (OPCs), respond differently to senescence, specifically contributing to the creation of a permissive niche for tumor recurrence. To contrast the effects of this phenomenon, a promising therapeutic strategy has emerged, the “one-two punch,” which induces initial DNA damage followed by selective elimination of senescent cells with senolytic drugs. In this review, we analyze in detail the efficacy of targeted synthetic agents, such as the Bcl-2 family inhibitor Navitoclax, and natural bioactive compounds such as Quercetin and Fisetin. The analysis focuses on the molecular mechanisms through which these agents disrupt anti-apoptotic pathways (SCAPs) and inhibit the PI3K/AKT/mTOR axis, restoring sensitivity to apoptosis. We propose that the integration of senolytic adjuvants into standard clinical protocols may represent a crucial frontier for eliminating residual disease reservoirs and we also suggest the possibility of combining them with molecules with neuroprotective action to significantly improve the prognosis in GBM. Full article

A recent publication proposed that the main biological function of chromosomal toxin–antitoxin systems (TASs) in free-living bacteria is to optimize fitness by mediating K Sensing and Control via a Nutrient-Responsive Cybernetic System. Viable cell density data were consistent with analog (continuous) regulation of population dynamics and cellular physiology throughout the life cycle; however, exactly how bacteria utilize TASs to regulate this was not explained in that publication. Two different concepts of injury have been proposed in the field of microbiology: (1) injury due to external physical and chemical stresses, which lead to sublethal (reversible) or lethal (irreversible) injury depending on the degree of injury, and (2) injury due to internal, self-inflicted stresses mediated by TA toxins. While self-inflicted injury due to TA toxins has been recognized as playing a role in growth arrest and dormancy, which can be reversed by repair, there is little support for TA toxins causing irreversible programmed cell death under normal physiological conditions. The purpose of the present paper was to explain how merging the above two concepts of injury might reveal how TASs optimize the fitness of free-living bacteria under normal physiological conditions by continuously regulating the ratio of injury: repair throughout the life cycle. Full article

This paper presents a physics-guided machine learning algorithm for classifying femur fracture presence and subtype using non-ionising radiofrequency (RF) spectral data. Multi-sensor S-parameter responses were generated from a femur phantom model across 1.0–3.0 GHz, producing 104 specimens representing intact bone and three fracture geometries. An exploratory, effect-size-driven band-selection algorithm identified a compact discriminative region between 1.74 and 1.90 GHz. Interpretable classifiers, including k-nearest neighbours (KNN), decision trees, linear discriminant analysis, and Naïve Bayes, were evaluated under strict specimen-level hold-out protocols to prevent data leakage. The KNN algorithm achieved 99.3% frame-level accuracy and 100% specimen-level accuracy for binary fracture detection while maintaining strong robustness in multiclass subtype classification, validated through sensor ablation and leave-one-subtype-out testing. The results demonstrate that compact, interpretable algorithms operating on band-limited RF spectra can achieve reliable, radiation-free fracture classification, supporting future development of continuous and edge-deployable monitoring systems. Full article

Hydrogen absorption kinetics in metal hydride beds is constrained by coupled heat and mass transfer, which often leads to a slow refueling response and reduced storage system efficiency. In this work, hybrid molding by mixing silicone gel with various thermally conductive additives was used to prepare TiMn-based metal hydride beds with tailored porosity and thermal conductivity. Three experimental groups were prepared: 5 wt.% silicone gel and 5 wt.% single-walled carbon nanotubes (Group A), 5 wt.% silicone gel only (Group B), and 5 wt.% silicone gel and 5 wt.% silicone sheets (Group C). Hydrogen absorption kinetics at 30 °C and 50 bar were measured experimentally and simulated using a coupled heat-mass transfer model in COMSOL Multiphysics. The physical property results showed that Group A exhibited approximately threefold higher porosity (0.527) compared with the other two groups, while its thermal conductivity (2.476 W·m⁻¹·K⁻¹) was the lowest among them (3.189 W·m⁻¹·K⁻¹ for Group B and 3.246 W·m⁻¹·K⁻¹ for Group C). These property differences led to distinct hydrogen absorption rate-limiting behaviors. Group A dominated in the diffusion-controlled stage (hydrogen uptake between 0.5 and 1.15 wt.%) due to enhanced hydrogen transport through its macroporous network, while Group C exhibited faster kinetics in the later stage (above 1.15 wt.%), where thermal conductivity governed the absorption driving force. Numerical simulations reproduced the experimental kinetic curves and confirmed the transition of rate-limiting mechanisms. This work reveals that the rate-limiting factors of hydrogen absorption in hybrid molding hydride beds vary across different stages, and that independent optimization of porosity and thermal conductivity is required to achieve rapid kinetics across the entire absorption process. Full article

Digital mining has become a tangible reality in recent years and the digital revolution enables and requires data exchange for autonomous machines and operational flow management. LoRa technology and its underground propagation behavior can make an important contribution to this digitalization. This paper presents a Data Mule approach that enabled progress in digitalization at refueling stations in active underground mining areas of a mine near Werra, Germany, operated by the K+S Group. This demonstration aimed to automate manual data collection at fuel gauges by using a dynamic LoRa network. We used specially developed LoRa Data Mule modules for operations over many square kilometers. LoRa was chosen for its industrial functionality and long-range capabilities, particularly in underground environments. The Data Mule modules used were in-house-designed units with underground mining-rated casing and connectors, as well as commercial LoRa boards and custom communication protocols. Connectivity between all systems was realized at travel speeds of 20 to 40 km/h, with connection data successfully relayed for 180 to 770 m, despite 90° turns and no line of sight. It was shown that the LoRa Data Mule approach can be used in a network of remote but active data generation points. Full article

Accurate, scalable estimation of rice planting dates is essential for climate-adaptive management in multi-cropping regions, yet most models rely on static calendars, which fail to capture climate-driven shifts and bias simulated yield responses. This study aims to develop a climate-driven, spatially explicit framework to simulate dynamic transplanting dates across diverse multi-cropping systems in monsoon Asia. Utilizing daily AgERA5 reanalysis and Monsoon Asia Rice Calendar (MARC) data from 2019 to 2020, we present Geo-ROCKET. The framework integrates an automated K-means clustering workflow to delineate bimodal planting windows and employs random convolutional kernel transforms with adaptive geographic neighborhoods to capture local climate heterogeneity. Evaluated by area-weighted mean absolute error (MAE), the model achieves high accuracy across six seasons (MAE 6.53–12.50 days), outperforming six traditional ROCKET and ensemble baselines while preserving smooth spatial error fields. Sensitivity experiments reveal that a 15-day bias in the previous harvest date can increase transplanting error to 10.8–17.8 days, emphasizing the importance of sequential consistency. By providing dynamic, climate-sensitive inputs, Geo-ROCKET improves the accuracy of crop modeling for climate impact projections. This framework offers a flexible tool for characterizing human management decisions and evaluating adaptation strategies in intensive agricultural systems. Full article