Search Results (1,230)

The integration of whole-cell biosensors in miniaturized measuring devices to exploit synergetic effects as small, rapid, cost-effective, sensitive, and highly specific platforms with point-of-care applicability was often discussed in recent years and many different setups have been presented to date. In many cases these setups were envisaged as powerful systems in their respective fields; however, the anticipated success often failed to materialize, and the systems remained a proof-of-concept. We elaborate on the hurdles and possible challenges that have to be overcome for the successful development and application of such systems. Further, we critically discuss and rank the impact of different challenges during system development, application, and commercialization. Finally, we point out possible future applications and conclude future perspectives for whole-cell biosensors integrated into microfluidic platforms. Full article

As the development of High-Speed Railways (HSRs) shifts from scale expansion to quality and efficiency, high-density timetables face increasing challenges regarding operational stability. Traditional capacity metrics often prioritize volume over service quality, neglecting the economic and service implications of delays. To reconcile theoretical capacity with practical reliability, this paper proposes a novel Reliable Value (RV)-oriented framework for HSR timetabling. We construct a Reserve Capacity Incremental Heuristic Optimization Framework that employs a synergetic integrated stochastic optimization strategy. This methodology treats reserve capacity as a systematically varied analytical parameter rather than a static constant, integrating redundancy layout planning with dynamic recovery adjustments under stochastic delay scenarios. The RV metric quantitatively combines efficiency (Expected Running Time) and robustness (Indirect Capacity Loss). A case study on the Beijing–Shanghai high-speed railway corridor demonstrates a non-linear relationship between reserve capacity allocation and system value. The results identify an optimal saturation interval of 5 to 14 min, where the marginal gains in reliability maximize the overall system value without excessively compromising operational efficiency. These findings provide theoretical support for transitioning from static capacity planning to proactive, value-based resilience engineering through optimized redundancy allocation. Full article

The precision of data gathered from Ultra-Weak Fiber Bragg Grating (UWFBG) sensing technology is limited when measuring strain within asphalt pavements. To better understand its measurement mechanism and correct possible errors, this study examines the synergy deformation behavior between UWFBG and asphalt mixtures under loads. Initially, the mesoscopic model of asphalt mixture containing UWFBG was constructed using a discrete element model, followed by the validation of the model. Then, the propagation of microcracks at the interface between the asphalt mixture and UWFBG was analyzed, revealing damage characteristics of this material under various loading stages. Additionally, a quantitative relationship between the crack width and the monitoring strain was identified. The significant effect of introducing the sensor on crack propagation and interface debonding in strain response was also highlighted. The results indicate that when displacement exceeds 1.4 mm during a bending test, the number of both damage and microcracks increases markedly, with cracks progressively developing. Especially at the UWFBG interface subjected to a tensile load, microcrack growth rises sharply, leading to the failure of the interface. The mor-UWFBG interface is not the main damage location, but it is the most vulnerable location to damage and may be the one affecting the monitoring of UWFBG. Without sensors, a consistent linear relationship between monitoring strain and crack width is observed within the asphalt mixture. After introducing the UWFBG sensor, the strain-crack response of the asphalt mixture is divided into three stages: crack initiation, crack propagation, and interface debonding. When the crack width surpasses 0.03 mm, interface debonding significantly influences the strain growth rate, indicating the necessity of correcting the synergy deformation. Full article

The inherent randomness, high volatility, and non-stationarity of photovoltaic (PV) power generation pose substantial threats to the stability of modern power grids. Developing high-precision forecasting models is essential for grid operation, yet conventional architectures often encounter a performance bottleneck: they struggle to simultaneously achieve high computational efficiency for long-range dependency modeling and robust perception for local, abrupt fluctuations. To address these limitations, this paper proposes HDCF-Mamba, a novel forecasting framework that resolves the feature distribution gap between long-range trends and short-term volatility. The core innovation lies in the Heterogeneous Dual-branch Cross-Fusion (HDCF) mechanism, which enables the synergetic integration of a Mamba-based global branch and a Multi-Kernel Filter Unit-based multi-scale local branch. Specifically, we integrate the Mamba Selective State Space Mechanism into the global branch to efficiently capture long-term dependencies with $O\left(L\right)$ linear complexity, fundamentally overcoming the quadratic computational bottleneck of Transformers. Meanwhile, the Multi-Scale Feature Extraction Module (MSFEM) acts as a local compensator to capture high-frequency power fluctuations caused by transient weather changes. Unlike simple hybrid models that rely on linear addition, our HDCF design utilizes a temporal concatenation mechanism to ensure non-linear alignment of these heterogeneous features. Extensive experiments on four real-world PV operational datasets (including publicly available benchmark datasets and actual photovoltaic power station monitoring data: ECD-PV, LSP-PV, APS-PV, and PSB-PV) demonstrate that HDCF-Mamba consistently outperforms state-of-the-art models, achieving a reduction in Mean Absolute Error (MAE) of up to 11.4% compared to iTransformer and 8% compared to SCINet, while maintaining superior computational efficiency. Full article

This paper investigates the dynamics of a permanent magnet synchronous motor (PMSM) and controls its chaotic speed behavior using the synergetic control technique (SCT). The model includes electrical dynamics in the dq frame and mechanical speed dynamics, with a scalar parameter $\gamma$ capturing cross-coupling effects. The equilibrium structure and local stability properties of the PMSM are analyzed. For zero input voltages and zero load torque, the system exhibits a pitchfork-type bifurcation in the electrical–mechanical equilibrium as $\gamma$ crosses a critical value. Explicit expressions are derived for all equilibria, and their stability is characterized using eigenvalue analysis and the Routh–Hurwitz criterion, and a secondary loss of stability via a Hopf-type mechanism is identified. The case of nonzero input voltages with zero load torque is also discussed. Numerical simulations confirm the analytical results and highlight the parameter regions that admit stable operation. Bifurcation diagrams show the different PMSM behaviors as the parameter $\gamma$ varies. For a certain interval of $\gamma$, the PMSM speed undergoes chaotic oscillations. The SCT is introduced to control the chaos. Macro variables are chosen to design the SCT. The derived SCT is implemented to eliminate the chaotic speed. The controller provides good performance in suppressing the chaos. The controller is tested under sudden reference speed change where the controller gets the new reference speed accurately. It is also evaluated under sudden and sinusoidal load torque variations. Full article

The Ordos Basin hosts significant shale gas resources in China, yet its marine-continental transitional sedimentary setting causes intense reservoir heterogeneity that severely hinders accurate sweet spot identification in the Permian Shanxi Formation. This study aims to reveal the synergistic controls of lithofacies, mineralogy, and organic matter on shale gas sweet spot formation in the southern Yishan Slope of the eastern Ordos Basin. A multi-dimensional characterization approach was adopted, integrating drilling/logging data and systematic core analyses including X-ray diffraction (XRD), organic geochemical testing, porosity/permeability measurement, and on-site gas content desorption, to quantify reservoir heterogeneity across lithofacies, mineralogy, organic geochemistry, and petrophysical properties. The results show that three lithofacies associations are identified in the target interval: mud-wrapped sand, sand-mud interbedding, and sand-wrapped mud, among which sand-mud interbedding and mud-wrapped sand associations exhibit higher total organic carbon (TOC) contents and strong inter/intra-well heterogeneity. The organic matter in the reservoir is dominated by Type III kerogen, with TOC values ranging from 0.04% to 12.15%, and the Shan 2 Member shows significantly higher average TOC (2.55%) than the Shan 1 Member (1.36%). The reservoir is characterized by ultra-low porosity (average of 0.77%) and low permeability (average of 0.26 × 10⁻³ μm²), with mesopores and macropores contributing over 99% of the total pore volume and showing a significant positive correlation with gas content. Quartz (average of 34.86%) and clay minerals present strong vertical heterogeneity, with the Shan 2 Member being more heterogeneous than the Shan 1 Member due to differences in sedimentary environment evolution. A TOC threshold of 1.5% is determined for sweet spot identification in the study area, and shale gas sweet spots are synergistically controlled by high TOC abundance, moderate brittle mineral content, and 0.1–3 m thick sandy interbeds. This study enriches the theoretical understanding of marine-continental transitional shale reservoirs and provides a scientific basis for sweet spot prediction and development optimization in similar heterogeneous shale gas systems worldwide. Full article

This work explores the application of a Category Theory model, advocating a paradigm for comprehending human experience and the communication process of a complex system from the perspective of a living Anticipatory System. Following the principles created by Robert Rosen for the anticipatory system and associated models—models that respect the principles of impredicativity, anticipation, and closure to efficient cause (CLEF)—we propose the Performance–Resilience–Sustainability (PRS) model. This new model introduces a new way to explain how anticipatory systems can elucidate the portions of variability observed in practice and research. Anticipatory system theory suggests that models such as PRS have significant potential to complement and explain dynamic phenomena observed in communication and experience development research, as well as in practical applications, underscoring the transformative potential for both fields. This class of models for complex systems may introduce a new dimension of emergent causality and its impact on current behavior, which was not previously considered. Full article

Dual-emission photoluminescence (PL) nanoprobes provide improved analytical performance to develop a reliable and sensitive sensing platform for quantifying chloramphenicol in pharmaceutical samples, thereby ensuring therapeutic efficacy and patient safety. In this work, a dual-emission PL sensing platform combining carbon dots (CDs) and AgInS₂ quantum dots (QDs) capped with mercaptopropionic acid (MPA) was developed for the quantitative determination of chloramphenicol, resorting to chemometric methods for data analysis. CDs, CdTe QDs, and AgInS₂ QDs were synthesized and individually evaluated considering their photostability, PL response and kinetics of their interaction with the antibiotic. After this, two dual-emission probes, CDs/MPA-CdTe and CDs/MPA-AgInS₂, were prepared and assessed based on the complementarity of their individual emission features. The obtained kinetic PL dataset was processed using unfolded partial least squares (U-PLS) in order to explore the multidimensional information of the dual-emission systems and to evaluate the performance of both sensing platforms. CDs/MPA-AgInS₂ probe was demonstrated to be the most efficient sensing platform due to its better compromise between sensitivity and photostability, as well as its cadmium-free composition, allowing the implementation of a more environmentally friendly analytical methodology. The optimization of the U-PLS models involved the assessment of the kinetic acquisition time and different spectral regions. The results showed that reliable, sensitive and efficient quantification could be achieved within the first 5 min of interaction and using the full emission spectrum of the sensing probe. Additionally, different interaction mechanisms were observed for each nanomaterial in the combined probe, being static for the CDs/chloramphenicol interaction and dynamic for MPA-AgInS₂/chloramphenicol interaction, which supports the synergetic behavior of the combined probe. The proposed methodology was effectively applied to commercial pharmaceutical formulations, yielding accurate results with good figures of merit. Therefore, this approach can be used as a relevant alternative to existing methodologies for a rapid, robust, and environmentally friendly method for chloramphenicol quantification. Full article

The sluggish reduction kinetics of thionyl chloride and the cathode passivation induced by the densification deposition of discharge product LiCl are critical challenges that severely hinder the commercialization of lithium/thionyl chloride (Li/SOCl₂) batteries. In this work, a dual-catalyst cobalt tetrapyridine porphyrin (CoTAP) and copper phthalocyanine (CuPc) supported on activated carbon (AC) were proposed to synergically regulate SOCl₂ reduction and product deposition. When the CoTAP/CuPc/AC catalyst was synthesized and applied as the cathode of Li/SOCl₂ batteries, UV-Vis spectroscopy, crystal field coordination structure analysis, DFT calculations and XPS measurements collectively demonstrated that CoTAP catalyzes SOCl₂ reduction through coordination at Co sites and strongly adsorbs Cl⁻, while CuPc features a weakly coordinated Cu center that facilitates the migration of LiCl products from the cathode surface. This collaborative effect in CoTAP/CuPc/AC cathodes effectively accelerates the reduction kinetics of SOCl₂ and promotes the ordered deposition of product LiCl, thereby guaranteeing the continuous and progressive discharge process in Li/SOCl₂ batteries. As a result, the CoTAP/CuPc/AC-catalyzed batteries exhibited excellent electrochemical performance with a stable discharge voltage of 3.16 V and high discharge capacity of 15.08 mAh, superior to the counterpart batteries without catalysts. This work provides a design idea for the development of advanced Li/SOCl₂ batteries. Full article

This study presents a hybrid modeling framework aimed at enhancing climate mitigation strategies by evaluating the predictive power of sustainability indicators using both statistical analysis—correlation metrics, regression modeling, distribution tests—and artificial neural networks (ANNs). The analysis centers on variables critical to climate outcomes, including renewable energy use in transport and electricity, greenhouse gas emissions from production, and aggregated target completion values. The findings identify renewable energy usage in transport as the primary predictor of improved performance in the Sustainable Development Report (SDR), followed by overall target completeness, electricity-based renewables, and production-related emissions. Multidimensional interaction analyses highlight a synergetic link between transport renewables and target achievement, underscoring their strategic relevance for climate mitigation efforts. The ANN models demonstrate high predictive accuracy and minimal error, affirming the model’s suitability for scenario-based climate forecasting. Results offer actionable intelligence for policymakers and climate stakeholders to optimize resource allocation and accelerate low-carbon transitions. The study acknowledges limitations, namely, the relatively small dataset and EU-centric analysis, and recommends future extensions to more geographically diverse datasets and the incorporation of advanced econometric techniques and AI frameworks to improve generalizability and predictive potency. Full article

To elucidate the microscopic adsorption mechanism of dodecylbenzenesulfonate (DBS⁻) on the surface of anatase TiO₂, this study was conducted based on density functional theory, using the DMol³ module for calculations. Four representative initial configurations including orientation differences in sulfonate, the benzene ring, and the alkyl chain were constructed. The contribution of each functional fragment to adsorption stability and interfacial electron transfer behavior were investigated through geometric optimization, energy calculation, Mulliken population, molecular electrostatic potential analysis, Fukui function, and density-of-states analysis. The results showed that configuration a-101 exhibited a lying orientation and multi-stage synergetic adsorption, with the largest adsorption energy (−210.29 kJ/mol), and it was the most stable configuration. The sulfonate group had the most negative electrostatic potential, and the highest occupied orbital was mainly located on its oxygen atom (O). Additionally, the f⁻ value of the Fukui function of O was the highest, serving as the key electrophilic reaction active site, and formed a Ti-O coordination bond with surface Ti⁴⁺. The benzene ring acted as an electron acceptor and participated in adsorption through π-d weak coupling. Adsorption induced the transfer of an about 0.7 e charge from DBS⁻ to TiO₂. The 2p orbitals of O and the 3d orbitals of Ti overlapped in the range of −5.0~0.45 eV, forming a coordination bond. Full article

Optimizing efficient electrode materials that combine high energy density, rapid charge transport, and excellent cycling stability remains a challenge for advanced supercapacitors. Here, we report the synthesis of an innovative copper(I)-based metal–organic framework (MOF), Cu₃(NDI)₃, prepared via a simple solvothermal method using N,N’-bis(3,5-dimethylpyrazol-4-yl)-naphthalene diimide (H₂NDI-H) as a linker. Structural analyses (XRD, FTIR, SEM, EDX, and BET) confirmed the formation of a highly crystalline, porous MOF. Integration of this MOF into laser-induced graphene (LIG) matrices yielded hybrid electrodes with enhanced structural characteristics and electrochemical activity, compared to its only-LIG counterpart. Electrochemical studies (CV, CD, EIS) revealed that the LIG–MOF electrode exhibited the highest performance, delivering a specific capacitance of 4.6 mF cm⁻² at 0.05 mA cm⁻², and an areal energy density of 60.03 μWh cm⁻² at a power density of 1292.17 μW cm⁻², outperforming both LIG and MOF–LIG configurations. This enhancement arises from the synergetic interaction between the conductive LIG network and the redox-active Cu₃(NDI)₃ framework, highlighting the potential of LIG–MOF hybrids as next-generation materials for high-performance supercapacitors. Full article

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Although cisplatin is widely used in chemotherapy, its clinical efficacy is often limited by adverse effects and resistance. Thus, natural bioactive compounds are gaining attention as complementary therapeutic agents. This study aimed to evaluate the anti-tumor effects of Annona muricata leaf extract on murine breast cancer 4T1 cells, used alone or in combination with cisplatin. Cisplatin induced intrinsic apoptosis through mitochondrial membrane disruption, up-regulation of the Bax gene and inhibition of the PI3K/AKT/mTOR signaling pathway. Cisplatin also promoted hypoxia by HIF1α gene expression, inflammation by TNFα and IL-6 gene expression, and induced cell cycle arrest at the sub-G1 phase by down-regulation of cyclin D1 and cyclin E1 genes. Annona muricata leaf extract triggered autophagy-mediated 4T1 cell death through mainly mTOR down-regulation and increased expression of Beclin1 and LC3 genes. It also induced cell cycle arrest at sub-G1 and S phases in a concentration- and time-dependent manner. When, combined with cisplatin, Annona muricata extract shifts the cell death pathway from intrinsic apoptosis toward autophagy by reduced caspase-3 gene expression and activity and enhanced LC3-I to LC3-II conversion. Moreover, Annona muricata extract attenuated cisplatin-induced inflammation by inhibiting TNFα and IL-6 gene expression and reinforced cell cycle arrest through suppression of the cyclin D1 gene. In conclusion, our results suggest that Annona muricata leaf extract exerts significant anti-tumor activity in breast cancer cells and may enhance cisplatin efficacy by shifting the signaling pathway from intrinsic apoptosis toward autophagy, and attenuating inflammation-related effects, supporting its potential use as a complementary therapeutic strategy. Full article

This paper reports the findings of the first initiative of developing a year-round streamflow forecasting system using the HBV hydrologic model in a data-scarce Ruvu catchment in Tanzania. Considering the importance of the Ruvu catchment as the main source of water to the fast-growing mega city of Dar es Salaam, the researchers in this study made the most of the available data and their joint previous application experience of the modelling framework for the purpose of setting up a reliable operational model. In addition, the researchers adopted a phased approach of developing the streamflow forecasting system, using HBV as a hydrological model, which resulted in a simplified model structure with minimized complexity. For instance, the snow routine was removed as it is not relevant to the study area, and a few parameters were reduced to improve model efficiency. As a measure to demonstrate model performance, in addition to the Nash–Sutcliffe Efficiency (NSE) parameter used for model calibration and verification, several other error functions and graphical displays were used. The model performance values, as measured by NSE for calibration and verification periods, are 0.85 and 0.82 for Ruvu Roadbridge (1H8A), and 0.80 and 0.82 for Kidunda (1H3), respectively, and all are classified as “Very Good”. In addition, the PBIAS of less than ±5% in calibration indicates excellent water balance simulation. Furthermore, the forecast’s performance in this study is evidenced by an annual forecast R² of 0.933, with operational meteorological forecasts improving to 0.962 with “perfect” precipitation; dry season performance with R² of 0.964, demonstrating high skill in baseflow-dominated periods; and the PBIAS for forecasts of 0.866, indicating a slight systematic under-forecasting correctable by a ~15% precipitation adjustment. Although the Ruvu catchment has been characterized by this study as a data-scarce catchment, the results of the operational hydrological forecasting system vary with season and quality of forecast meteorological data, and the model is already launched for operational use. As evidenced by these study findings, the journey from data scarcity to operational forecast provision in the Ruvu catchment demonstrates that the principal barriers are fundamentally institutional and capacity-related. The authors suggest that any future forecasting initiative should put much emphasis on both the understanding of the modelling framework to be used and adequate data collection and analysis, in a synergetic manner with all relevant agencies. And it is also recommended to be vigilant regarding changes in the catchment characteristics and model performance during its life cycle, as the performance of the developed model is only valid under the condition that it was calibrated and validated. Full article