Search Results (1,522)

A parallel bacterial foraging algorithm was developed for the multiple sequence alignment problem. Four sets of homologous genetic and protein sequences related to Alzheimer’s disease among various species were collected from the NCBI database for convergence analysis and performance comparison. The main question was the following: is the bacterial foraging algorithm suitable for the multiple sequence alignment problem? Three versions of the algorithm were contrasted by performing a t-test and Mann–Whitney test based on the results of a 30-run scheme, focusing on fitness, execution time, and the number of function evaluations as performance metrics. Additionally, we conducted a performance comparison of the developed algorithm with the well-known Genetic Algorithm. The results demonstrated the consistent efficiency of the bacterial foraging algorithm, while the version of the algorithm based on gap deletion presented an increased number of function evaluations and excessive execution time. Overall, the first version of the developed algorithm was found to outperform the second version, based on its efficiency. Finally, we found that the third bacterial foraging algorithm version outperformed the Genetic Algorithm in the third phase of the experiment. The sequence sets, the algorithm’s Python 3.12 code and pseudocode, the data collected from the executions, and a GIF animation of the convergence on various different sets are available for download. Full article

Background/Objectives: Cancer research aims to understand the cellular and molecular mechanisms involved, in order to identify new therapeutic targets and provide patients with more effective therapies that generate fewer side undesirable and toxic effects. Previous studies have demonstrated the role of small nucleolar RNAs (snoRNAs) in many physiological and pathological cellular processes, including cancers. SnoRNAs are a group of non-coding RNAs involved in different post-transcriptional modifications of ribosomal RNAs. Recently, we identified a new snoRNA (jouvence), first in Drosophila, and thereafter, by homology, in humans. Methods: Here, we characterize the effect of the knockdown of jouvence by a sh-lentivirus on human primary patient-derived glioblastoma cells. Results: The sh-lentivirus anti-jouvence induces a significant decrease in cell proliferation and leads to cell death. EdU staining confirmed this decrease, while TUNEL also showed the presence of apoptotic cells. An RNA-Seq analysis revealed a decrease, in particular, in the level of BAALC, a gene known to potentiate the oncogenic ERK pathway and deregulating p21, leading to cell cycle blockage. Conclusions: Altogether, these results allow the hypothesis that the knockdown of jouvence could potentially be used as a new anti-cancer treatment (sno-Therapy), especially against glioblastoma and also, potentially, against acute myeloid leukemia (AML) due to the BAALC deregulation. Full article

Background: Diabetic foot disease is a public health problem. The challenges of its management lie in the complexity of wound healing and, in particular, the high rate of lesion recurrence. Objectives: The primary objective of the study was to evaluate whether optimized post-healing follow-up by a multidisciplinary team can reduce the recurrence rate of foot ulcers in people living with diabetes. The secondary objectives were to assess patient needs in terms of hospitalization for recurrence, the number of amputations, pedicure care, and the use of adapted footwear. Participants: The study included 129 patients with diabetes presenting a healed foot ulcer. A total of 38 patients underwent an annual post-healing follow-up visit with a multidisciplinary team (optimized follow-up), while 91 had a visit every 2 years (minimum follow-up). Results: Of the 38 patients with optimal follow-up, 8 presented a wound recurrence (21.1%) compared with 38 out of 91 patients (41.8%) receiving minimum follow-up. The recurrence rate decreased significantly between the two groups (p < 0.05). The use of adapted shoes was also significantly better in the group with optimized follow-up (p = 0.02). Conclusions: Regular post-healing follow-up with a multidisciplinary team seems to be a contributing factor to reducing the recurrence of diabetic foot ulcers among people living with diabetes. Full article

Glass curators often question how their treatments affect the long-term stability of historical glass. While damp cotton swabs are commonly used to remove surface salts and dust, the use of water remains controversial, particularly for heavily altered glass, due to concerns about worsening hydration. This study investigates the effect of water rinsing on an unstable soda-lime glass altered for six months (monoliths) and fifteen months (powders) at 35 °C and 85% relative humidity. Samples were then rinsed with Milli-Q water at 20 °C or 50 °C, and the monolithic glass was subsequently subjected to an additional 15 months of alteration under the same conditions. The glass surface was characterized by optical and scanning electron microscopy (SEM) as well as Raman spectroscopy to identify the nature of the salts. The evolution of the hydrated layer was assessed using transmission FTIR, Raman and solid-state NMR spectroscopies, ToF-SIMS, and thermogravimetric analysis (TGA). The results show that rinsing effectively removes surface salts—primarily sodium carbonate—and induces structural changes in the hydrated layer, promoting silicate network polymerization. Upon resuming alteration, rinsed monolithic samples exhibit no further degradation after the additional 15 months of alteration. These findings offer promising insights for conservation practices and may help curators refining their treatment strategies for altered glass. Full article

With the advent of Autonomous Vehicles (AV) technology, extensive research around the design of on-demand mobility systems powered by such vehicles is performed. An important part of these studies consists in the evaluation of the economic impact of such systems for involved stakeholders. In this work, a cost–benefit analysis (CBA) is applied to the introduction of AV services in Paris-Saclay, an intercommunity, south of Paris, simulated through MATSim, an agent-based model capable of capturing complex travel behaviors and dynamic traffic interactions. AVs would be implemented as a feeder service, first- and last-mile service to public transit, allowing intermodal trips for travelers. The system is designed to target the challenges of public transport accessibility in periurban areas and high private car use, which the AV feeder service is designed to mitigate. To our knowledge, this study is one of the first CBA analyses of an intermodal AV system relying on an agent-based simulation. The introduction of AV in a periurban environment would generate more pressure on the road network (0.8% to 1.7% increase in VKT for all modes, and significant congestion around train stations) but would improve traveler utilities. The utility gains from the new AV users benefiting from a more comfortable mode offsets the longer travel times from private car users. A Stop-Based routing service generates less congestion than a Door-to-Door routing service, but the access/egress time counterbalances this gain. Finally, in a periurban environment where on-demand AV feeder service would be added to reduce the access and egress cost of public transit, the social impact would be nuanced for travelers (over 99% of gains captured by the 10% of most benefiting agents), but externality would increase. This would benefit some travelers but would also involve additional congestion. In that case, a Stop-Based routing on a constrained network (e.g., existing bus network) significantly improves economic viability and reduces infrastructure costs and would be less impacting than a Door-to-Door service. Full article

In this article, we describe an approach to teaching introductory quantum mechanics and machine learning techniques. This approach combines several key concepts from both fields. Specifically, it demonstrates solving the Schrödinger equation using the discrete-variable representation (DVR) technique, as well as the architecture and training of neural network models. To illustrate this approach, a Python-based Jupyter notebook is developed. This notebook can be used for self-learning or for learning with an instructor. Furthermore, it can serve as a toolbox for demonstrating individual concepts in quantum mechanics and machine learning and for conducting small research projects in these areas. Full article

Amyloids are protein aggregates having a cross-β structure, and they reveal some unusual properties, like interactions with specific dyes and resistance to actions of detergents and proteases, as well as the capability to force some proteins to change their conformation from a soluble form to aggregates. The occurrence of amyloids is not restricted to humans and animals, as they also exist in microbial cells. However, contrary to animals, where amyloids are usually pathological molecules, bacterial amyloids are often functional, participating in various physiological processes. In this review, we focus on a specific property of bacterial amyloids, namely their ability to interact with nucleic acids and resultant regulatory mechanisms. Moreover, some of these interactions might play indirect roles in the pathomechanisms of human neurodegenerative and inflammatory diseases; these aspects are also summarized and discussed in this review. Full article

This paper introduces a novel end-to-end fault diagnosis framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with Time-Domain Reflectometry (TDR) for the detection, characterization, and localization of wiring faults. The method is designed to operate directly on TDR signals, requiring no manual feature extraction or preprocessing. A forward model is used to simulate TDR responses across various fault scenarios and topologies, serving as the basis for supervised learning. The proposed BiLSTM-based model is trained and validated on common wiring network topologies, demonstrating high diagnostic performance. Experimental results show a diagnostic accuracy of 98.97% and a macro-average sensitivity exceeding 98%, outperforming conventional machine learning techniques. In addition to technical performance, the proposed approach supports sustainable and predictive maintenance strategies by reducing manual inspection efforts and enabling real-time automated diagnostics. Full article

Over the last decade, numerous impedance studies of the conductivity of suspensions containing colloidal (dielectric, semiconducting or metallic) particles have often led to the conclusion that the well-known Maxwell theory is insufficient to quantitatively explain the properties of these systems. We review some of the most characteristic results and show how the applicability of the Maxwell’s theory can be restored taking into account the adsorption phenomena occurring during AC impedance measurements in nanoparticle suspensions. The latter can drastically change the capacitance of the metal-electrolyte cell boundaries from the standard value, making it strongly dependent on the nanoparticle concentration. This factor significantly affects conductivity measurements through RC circuit characteristics. We present an analysis of available impedance measurement data of the dependence of conductivity on the nanoparticle concentration in this new paradigm. In order to emphasize the novelty and the acute sensitivity of ac-diagnosis to the presence of adsorption phenomena at the metal-electrolyte interface, direct adsorption determinations at such interfaces by using two modern experimental techniques are also presented. The main result of this work is the restoration of Maxwell’s theory, attributing the observed discrepancies to variations in cell conductance. Full article

This study assesses the use of Quantile-Mapping methods for bias correction and downscaling in climate change studies. A “Perfect Model Experiment” is conducted using high-resolution climate simulations as pseudo-references and coarser versions as biased data. The focus is on European daily temperature and precipitation under the RCP 8.5 scenario. Six methods are tested: an empirical Quantile-Mapping approach, the “Cumulative Distribution Function—transform” (CDF-t) method, and four CDF-t variants with different parameters. Their performance is evaluated based on univariate and multivariate properties over the calibration period (1981–2010) and a future period (2071–2100). The results show that while Quantile Mapping and CDF-t perform similarly during calibration, significant differences arise in future projections. Quantile Mapping exhibits biases in the means, standard deviations, and extremes, failing to capture the climate change signal. CDF-t and its variants show smaller biases, with one variant proving particularly robust. The choice of discretization parameter in CDF-t is crucial, as the low number of bins increases the biases. This study concludes that Quantile Mapping is not appropriate for adjustments in a climate change context, whereas CDF-t, especially a variant that stabilizes extremes, offers a more reliable alternative. Full article

The Eemian interglacial (~130–116 ka) is a period characterized by a significantly warmer climate than the pre-industrial era, providing a valuable opportunity to study natural climate variability and its implications for the future. We studied the Eemian climate in Europe by applying an interactive downscaling to our Earth system model (iLOVECLIM) to increase its horizontal atmospheric resolution from 5.56° to 0.25° latitude-longitude. A transient simulation was conducted for both the standard version of the model and with an interactive downscaling applied for the Eemian (127–116 ka). Our simulations suggest that the magnitude of temperature and precipitation varied across different regions of Europe, with some areas experiencing more pronounced warming and precipitation changes than others. The latitudinal pattern in our simulation during the Eemian shows that the warming in Europe was stronger at high latitudes than at mid-latitudes. Relative to the pre-industrial climate, our downscaling scheme simulates at 127 ka higher temperatures between 3–4 °C in the northern part of Europe and higher precipitation values between 150–300 mm/yr. Our results indicate that, in comparison to the standard model, the downscaled simulations offer spatial variability that is more in line with proxy-based reconstructions and other climate models. Full article

Hydrogen is gaining prominence as a clean energy vector, yet its extreme flammability demands robust detection solutions for industrial safety. In this study, we present the development and experimental validation of a microwave hydrogen gas sensor based on a patch-type microstrip antenna with a silver sensing element. The device operates at 5.99 GHz and was tested under controlled environmental conditions (humidity: 20 ± 0.4%, temperature: 27 ± 0.2 °C). Hydrogen exposure induces measurable shifts in the antenna’s resonant frequency due to dielectric modulation of the silver layer. The sensor exhibited a linear sensitivity of 3 kHz/ppm in the 310–600 ppm concentration range, with a residual standard deviation of 31.1 kHz and a calculated limit of detection (LOD) of approximately 31 ppm. The reflection coefficient remained below −10 dB throughout, confirming that the antenna maintains functional RF performance during sensing. These results demonstrate the sensor’s dual functionality for gas detection and communication, offering a compact and scalable platform for hydrogen safety monitoring. Full article

The pressure dependence of structural behavior in the orthorhombic (Pccn, PI) and monoclinic (P2₁/c, PII) polymorphs of the compound [Fe(PM-BiA)₂(NCS)₂], where PM–BiA = (N–(2′–pyridylmethylene)–4-amino–bi–pheynyl), is studied with synchrotron single-crystal X-ray diffraction and vibrational spectroscopy. Both polymorphs are stable up to ∼1.5 GPa, with a spin state transition occurring only in polymorph PII under hydrostatic conditions as documented by single-crystal synchrotron diffraction. The diffraction data also provide evidence of the formation of superstructures for both PI, with a doubled c axis, and PII, with a doubled b axis, on applying pressures above 2 GPa. The LS and HS states seem to coexist at high-pressures for both polymorphs studied with synchrotron infrared spectroscopy at quasi-hydrostatic conditions. Such results indicate that the occurrence of spin-crossover transformations in [Fe(PM-BiA)₂(NCS)₂] might strongly depend on the stress in the sample. Full article

Electric vehicles are increasingly being used for green transportation in smart urban mobility, thus protecting environmental biodiversity and the ecosystem. Energy storage by electric vehicle batteries is a critical point of this ecologically responsible transportation. This storage is strongly linked to the different external managements related to its capacity state. The latter concerns the interconnection of storage to energy resources, charging strategies, and their complexity. In an ideal urban context, charging strategies would use wireless devices. However, these may involve complex frames and unwanted electromagnetic field interferences. The sustainable management of wireless devices and battery state conditions allows for optimized operation and minimized adverse effects. Such management includes the sustainable design of devices and monitoring of complex connected procedures. The present study aims to analyze this management and to highlight the mathematical routines enabling the design and control tasks involved. The investigations involved are closely related to responsible attitude, “One Health”, and twin supervision approaches. The different sections of the article examine the following: electric vehicle in smart mobility, sustainable design and control, electromagnetic exposures, governance of physical and mathematical representation, charging routines, protection against adverse effects, and supervision of complex connected vehicles. The research presented in this article is supported by examples from the literature. Full article

Polarized Small-Angle Neutron Scattering is a versatile low-energy neutron scattering technique that allows for the access of magnetic information on nanosize objects of size 2–100 nm, from individual properties like the magnetization distribution inside the object to the collective behaviors, e.g., spin-glass effects or long-range magnetic ordering. The multi-scale possibilities of this technique is particularly relevant to encompass simultaneously the individual and collective many-body phenomena. In this article, we report the direct measurement of the magnetic form factor of “Prussian Blue Analog” molecular-based Ferromagnetic nanoparticles ${\mathrm{Cs}}_{\mathrm{x}}^{\mathrm{I}}{\mathrm{Ni}}^{\mathrm{II}}\left[{\mathrm{Cr}}^{\mathrm{III}}{\left(\mathrm{CN}\right)}_6\right]$ embedded in a polymer matrix with use of Polarized Small-Angle Neutron Scattering. We show that PSANS is particularly adapted to evaluate the internal magnetization distribution in nanoparticles and determine their magnetic morphology. Full article