Despite promising performance at ambient temperature, the development of aqueous zinc batteries is jeopardized by the freeze of aqueous electrolytes and deteriorative electrode‐electrolyte interphase at low temperatures. Herein, inspired by the cryoprotective mechanism of extracellular polysaccharides in biological organisms, a quaternized galactomannan polysaccharide (q‐GPA) is proposed as a cryoprotective additive for improving low‐temperature performance of aqueous zinc batteries. Mechanistic studies revealed that a multi‐hydroxyl galactomannan backbone can substantially attenuate the activity of water molecules through the reconfiguration of the hydrogen bond network, which inhibits ice crystal formation at subzero temperatures and thus depress the freezing point of the electrolyte. Meanwhile, the quaternary ammonium groups tethered on the q‐GPA skeleton are intended to neutralize the interfacial electric field through electrostatic repulsion, thereby accelerating Zn2+ deposition kinetics and prohibiting zinc dendrite growth. Impressively, the q‐GPA–modified electrolyte enables an extended lifespan of over 1700 h in Zn||Zn symmetric battery at a high current density of 3 mA cm−2 and an ultralong cycle life of 5000 cycles with a capacity retention of 99.2% in the Zn||Na2V6O16·1.5H2O (NVO) full battery at −30 °C. This work provides unprecedented possibilities for optimizing the electrolyte formulation of low‐temperature aqueous batteries.
Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles’ velocity using the randomly generated angles, which enhances the algorithm’s searchability and avoids premature convergence. It is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Transit Search (TS) algorithms by benchmark functions. It has great performance in unimodal optimization problems, and it gains the best fitness value with fewer iterations and average runtime than other algorithms. The Q-learning method is implemented for local path planning to avoid moving obstacles and combines with the proposed BPSO for the safe operations of automated guided vehicles. The presented BPSO-RL algorithm combines the advantages of the swarm intelligence algorithm and the Q-learning method, which can generate the globally optimal path with fast computational speed and support in dealing with dynamic scenarios. It is validated through computational experiments with moving obstacles and compared with the PSO algorithm for AGV path planning.
We propose a novel framework for modeling thermal transport in biological tissues based on a fractional bio-heat diffusion equation regularized by a generalized (q, τ)-entropy functional. The model incorporates a Caputo-Numerical simulations demonstrate the evolution of temperature profiles and entropy dynamics, revealing the interplay between fractional memory, metabolic heat generation, and entropy-induced resistance. A stability theorem this framework offers a physically consistent and flexible approach grounded in non-equilibrium statistical mechanics and bio-thermal regulation, making it suitable for applications in complex biological media with long-range.
In this work, a two-dimensional ring-shaped photonic crystal structure is proposed. The proposed structure is consisted of 28*25 silicon rods situated in air medium. Fundamental properties of the proposed structure (acting as a biosensor) are investigated through photonic bandgap (PBG) and field distribution diagrams. The proposed structure is considered for diagnosis of cholesterol and creatinine levels in blood samples. Proposed structure is designed to operate as a 2:1 multiplexer (also functioning as a biosensor). Thus, by inserting incident signals with specific wavelengths (placed in TE PBG region) to input0, input1 and select ports, light wave can be transmitted to output port. Cholesterol concentrations in blood sample can be detected by considering input0 (I0 = 1), input1 (I1 = 0) and select (S = 0). For cholesterol following bio sensing factors will be calculated. Q: (45.4–52.88), S: 2673.4 nm/RIU, DL: (0.00125–0.00143) RIU, FOM: (80.91–82.06) RIU−1. Creatinine levels in blood samples can also be diagnosed by considering input0 (I0 = 0), input1 (I1 = 1) and select (S = 1). For creatinine, Q: (101.1–109.4), S: 3582.7 nm/RIU, DL: (4.98e−4–5.26e−4) RIU and FOM: (199.01–201.3) RIU−1 will be calculated. Finally, proposed system can effectively help physician in early and precise prognosis of hypercholesterolemia and acute kidney injuries.
S. Nimmala, Ravindrareddy Chilukuri, Shaik Janbhasha
et al.
The rapid expansion of 5G networks necessitates sophisticated resource allocation algorithms to tackle the problems posed by fluctuating traffic conditions, varied device requirements, and rigorous Quality of Service (QoS) standards. This work presents the Bio-AI Allocator, a hybrid model that combines Deep Reinforcement Learning (DRL) with Ant Colony Optimization (ACO) for effective and adaptable resource management. The DRL model is trained via the publicly accessible 5G Quality of Service Dataset from Kaggle, which includes essential parameters like as signal strength, bandwidth utilization, latency, and user mobility patterns. The training utilizes Q-learning with episodic incentives to formulate optimal resource allocation strategies. Experimental findings indicate that the Bio-AI Allocator realizes a 20% decrease in latency, a 25% increase in throughput, and a 15% gain in energy economy relative to traditional approaches such as Round-Robin and Max-Min Fairness scheduling. The comparative analysis demonstrates the superiority of the proposed hybrid model compared to standalone AI and bio-inspired methods, proving the Bio-AI Allocator is a scalable and intelligent solution for next-generation 5G networks.
Bound states in the continuum (BIC) present a novel avenue for advancing high-quality factor metasurfaces, promising in high-performance lasers, sensors, and nonlinear optical devices at the nanoscale. Currently, sensors designed based on BIC have achieved good sensing performance. However, the functionality of current metasurface sensors is relatively singular, rendering them less chance in complex sensing scenarios. Specifically, taking a bio-enzyme metasurface sensor as an example, since different bio-enzymes have different optimal reaction temperatures, it is mostly inescapable to design multiple metasurface sensors for different bio-enzyme detection. In this paper, we developed a multifunctional sensor that can adapt to different reaction temperatures of bio-enzymes, meeting the requirements of multiple scenarios. The proposed metasurface consists of two elliptical cylinders, which can excite a high-Q quasi-BIC resonance by changing their rotation angles. By introducing VO2 film, external ambient temperature can effectively manipulate the transmission modulation depth and quasi-BIC. Simulation results show the maximum relative modulation depth of the metasurface can reach 296%. When combined with bio-enzymes, the metasurface serves as a refractive index sensor with a sensitivity as high as 370 nm RIU−1 at 30 °C and 80 °C. Our work provides insights for the design of highly integrated and tunable devices in the future.
The increasing significance of Vehicular Ad‐hoc Networks (VANETs) in intelligent transportation systems has introduced challenges related to high mobility, network congestion, and energy efficiency. To address these challenges, this paper proposes a new approach based on Delay‐Minimization and Back‐Off Aware Q‐Learning with Advanced Bio‐Inspired Cluster Head (CH) Selection (DBACH) to enhance multi‐hop data transmission in VANETs. The DBACH framework features network formation, latency minimization, a back‐off Q‐learning model, and an improved dragonfly algorithm‐based CH selection. This method reduces transmission delay, routing overhead, and power consumption to enhance VANET QoS. DBACH was evaluated against RCDC, DCPA, and WCAM for effectiveness. The simulated vehicle numbers and speeds (km/h) were used to assess energy efficiency, throughput, packet delivery ratio, data loss ratio, computation time, and routing overhead. The DBACH boosts energy efficiency to 85 J, throughput to 160–200 Kbps, and packet delivery ratio to 11%–13%. Data loss drops to 7%–15%, latency is 60–94 ms, and routing overhead is 170–300 packets. When DBACH is a promising option for enhancing VANET communication dependability and energy economy due to its efficiency, communication stability, and success rates.
Ernesto Sanz, Andrés F. Almeida‐Ñaulay, Carlos G. H. Díaz‐Ambrona
et al.
Abstract Understanding the dynamics of the soil–vegetation–atmosphere (SVA) system, particularly in arid and semiarid regions, remains challenging due to its intricate and interdependent nature. This system creates problems for rangeland administration, such as insurance and risk management. This paper focuses on the complex interactions within the SVA system, particularly on rangeland ecosystems in Spain's semiarid and arid regions. By employing multifractal detrended cross‐correlation analysis (MFCCA), we explore the joint behavior of key variables, including precipitation (PCP), evapotranspiration (ETP), aridity index (Arid. I.), soil water availability (SWA), biomass (Bio), and normalized difference vegetation index (NDVI). Analyzing a 20‐year data series from Madrid and Almeria provinces, we reveal distinct patterns in the studied variables’ persistence, multifractality, and asymmetry. Notably, the differences in the generalized Hurst exponents (hxy(q)) between Madrid and Almeria for SWA with NDVI, SWA with Bio, and NDVI with Bio underscore distinct interactions in these regions. Moreover, multifractal analyses unveil differences in the complexity of joint variables’ behaviors in the two regions. Almeria exhibits higher multifractality across variables, indicating more complex and variable environmental interactions, likely due to its more arid conditions. These findings suggest that Almeria has more sensitivity to changes, requiring adaptive management strategies, while in Madrid, water availability and related variables play a more dominant role in driving vegetation dynamics. These findings shed light through MFCCA on the nuanced dynamics of rangeland ecosystems in semiarid and arid regions, emphasizing the importance of considering complexity‐based approaches to understand the intricate interplay among key variables in the SVA system.
Abstract The aim of this study was to assess the association between thyroid function and perceived anxiety in non-depressed older adults. Non-depressed Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with complete Thyroid Stimulating Hormone (TSH) and neuropsychiatric inventory (NPI/NPI-Q) were included. The association between anxiety and thyroid function was assessed by logistic regression and sex stratification. Restricted cubic splines were applied to evaluate non-linearity in the association. The median age of 2,114 eligible participants was 73 years (68–78), 1,117 (52.84%) were males, and the median TSH was 1.69 µIU/mL. There was a significant association between TSH and informant-perceived anxiety in the total study population (ORModel1 = 0.86, 95%CI 0.76–0.97, p = 0.011), even after adjusting for bio-demographical (adj.ORModel2 = 0.85, 95%CI 0.75–0.96, p = 0.007), and socio-cognitive confounders (adj.ORModel3 = 0.84, 95%CI 0.73–0.96, p = 0.009). Sex-stratification showed similar significant results in all male-specific models (ORModel1-male = 0.71, 95%CI: 0.58–0.85, p Model1-male < 0.001). In the general population and males, a TSH value of 2.4 µIU/dL was a significant cutoff under which anxiety odds were significantly high, even after adjusting for confounders. The sex-dependent association between TSH levels and perceived anxiety in non-depressed older adults is a novel finding that has to be further explored for a better understanding of the underlying neurobehavioral biology.
Abstract Xanthomonas is a genus of plant-associated Gram-negative bacteria which infect more than 400 plant species. A characteristic feature of Xanthomonas bacteria is the production of yellow membrane-bound pigments called xanthomonadins. Xanthomonadins are phospholipid-like bio-macromolecules located at the outer membrane. The chemical structure and biosynthetic mechanism of xanthomonadin production remain to be fully elucidated. In this study, a total of 24 Xanthomonas strains from five different species were collected for methylated ester of aryl polyene (MEAP) preparation. High-Performance Liquid Chromatography (HPLC) and Quadrupole Time-of-Flight Mass Spectrometry (Q-TOF–MS) analysis identified three dominant MEAPs, methylated di-brominated MEAP-1, di-brominated MEAP-2, and mono-brominated MEAP-3. MEAP-1 corresponded to the previously reported aryl polyene in Xanthomonas juglandis XJ103. The 24 Xanthomonas strains could be grouped into three categories based on their MEAP profiles. Further, bacterial ooze was collected from X. oryzae pv. oryzae (Xoo)-infected rice leaves and MEAP was prepared. The dominant MEAP in the Xoo ooze was MEAP-2. This is the first demonstration of in-planta MEAP production during plant infection of any Xanthomonas pathogen. In addition, a xan biosynthetic cluster, which is responsible for xanthomonadin biosynthesis, and the roles of the individual xan genes in MEAP biosynthesis were studied via deletion and subsequent complementation analysis. HPLC and Q-TOF–MS analysis identified the essential genes for MEAP biosynthesis, as well as the genes associated with methylation and bromination. These results provide new insights into the structural diversity of Xanthomonas MEAPs and xanthomonadin biosynthetic mechanisms.
The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.
Target discovery of natural products is a key step in the development of new drugs, and it is also a difficult speed-limiting step. In this study, a traditional Chinese medicine microspheres (TCM-MPs) target fishing strategy was developed to discover the key drug targets from complex system. The microspheres are composed of Fe3O4 magnetic nanolayer, oleic acid modified layer, the photoaffinity group (4- [3-(Trifluoromethyl)-3H-diazirin-3-yl] benzoic acid, TAD) layer and active small molecule layer from inside to outside. TAD produces highly reactive carbene under ultraviolet light, which can realize the self-assembly and fixation of drug active small molecules with non-selective properties. Here, taking Shenqi Jiangtang Granules (SJG) as an example, the constructed TCM-MPs was used to fish the related proteins of human glomerular mesangial cells (HMCs) lysate. 28 differential proteins were screened. According to the target analysis based on bioinformatics, GNAS was selected as the key target, which participated in insulin secretion and cAMP signaling pathway. To further verify the interaction effect of GNAS and small molecules, a reverse fishing technique was established based on bio-layer interferometry (BLI) coupled with UHPLC-Q/TOF-MS/MS. The results displayed that 26 small molecules may potentially interact with GNAS, and 7 of them were found to have strong binding activity. In vitro experiments for HMCs have shown that 7 active compounds can significantly activate the cAMP pathway by binding to GNAS. The developed TCM-MPs target fishing strategy combined with BLI reverse fishing technology to screen out key proteins that directly interact with active ingredients from complex target protein systems is significant for the discovery of drug targets for complex systems of TCM.
R. M. Zulqarnain, Hamza Naveed, Sameh S. Askar
et al.
Selecting an optimum technique for disposing of biomedical waste is a frequently observed obstacle in multi-attribute group decision-making (MAGDM) problems. The MAGDM is commonly applied to tackle decision-making states originated by obscurity and vagueness. The interval-valued q-rung orthopair fuzzy soft set is a novel variant of fuzzy sets. The main objective of this study is to introduce the interval-valued q-rung orthopair fuzzy soft Einstein-ordered weighted and Einstein hybrid weighted aggregation operators. Based on developed aggregation operators, a novel decision-making approach, the Evaluation based on the Distance from the Average Solution introduced to solve the MAGDM problem. The execution of the proposed approach demonstrates the significant impact of determining the most effective strategy to handle biomedical waste. Our proposed approach's practicality is confirmed by a case study focusing on selecting the most effective technique for Biomedical Waste (BMW) treatment. This study shows that autoclaving is the most effective method for the disposal of BMW. Comparative and sensitivity analysis confirms the consistency and effectiveness of our methodology. The comparative study indicates the effects of the proposed strategy are more feasible and realistic than the prevailing techniques.
Jian Wang, Nehad Ali Shah, Bander Almutairi
et al.
Heat transfer has a major effect on material selection and mechanical efficiency. The importance of peristaltic fluid motion in transmitting heat is obvious in the domains of biomedical research through the processes of metabolic heat generation, blood transportation, capillary dehydration, thermal control, and bio-heat exchange pathways. The present work aims to explore the computational and theoretical evaluation of hybrid nanofluid (HNF)-suspended Cu and Al2O3 nanoparticles in water in the presence of chemical reaction, Lorentz force and ross-diffusion. The fundamental flow system based on Navier-Stokes in terms of partial differential equations is converted to dimensionless nonlinear ordinary differential equations via similarity transformations. The transformed ODEs are computationally solved by bvp4c approach. Dual solutions have been observed for emerging factors, so stability examinations are implemented to find the stable solution. Based on eigenvalues, it is witnessed that smallest positive eigenvalues indicates the stable while negative depicts the unstable solutions. The influences of involved factors on the flow characteristics are depicted through graphs and tables. The confirmation of the present analysis is done with the published study. Decreasing behavior is investigated for both branches of velocity as the M is improved while f′(η) is an increasing function of K. From this analysis it is reported that θ(η) is directly proportional to R, Q and Q1. Increasing phenomena is reported for θη and ϕη profiles as the quantities of Ec are augmented from 0.1 to 1. The conclusions of this theoretical analysis have impending applications in numerous fields such as technological devices, solar cooling and heating systems for cars, surfactants, lubricating qualities, hybrid generators, metallic welding process, and the production of medical equipment.
Erica Fiorbelli, Marco Lapris, Michela Errico
et al.
To protect ruminants from the harmful effects of mycotoxins, anti-mycotoxin agents can be added to the dietary ration, thus guaranteeing animal health and production. Therefore, the objective of this study was to evaluate the in vitro ruminal initial sequestration (weak binding) and subsequent desorption (strong binding) of an anti-mycotoxin agent based on a mixture of adsorbing material, turmeric and milk thistle extracts and yeast-based components to adsorb or bio-convert aflatoxins (AF), fumonisins B1 and B2 (FB), trichothecene deoxynivalenol (DON), T-2 and HT-2 toxins, and zearalenone (ZEN). Two doses were tested: Dose 1 simulated 30 mg/cow/d, while Dose 2 simulated 90 mg/cow/d of the anti-mycotoxin agent. Each treatment involved three analytical replicates at each of three incubation times (1, 4, and 24 h post-incubation), with two independent experimental runs providing experimental replicates. Analytical methods, including UHPLC-HRMS and multivariate analyses, were used to both quantify mycotoxin concentrations and reveal dose-dependent reductions, with statistical validations indicating significant changes in mycotoxin levels across both dose and time. The results indicated that the anti-mycotoxin agent was able to highly bind AFB1, T2, and HT-2 toxins since its concentration was always under the limit of detection (<1 ppb). Regarding ZEN (weak binding mean: 94.6%; strong binding mean: 62.4%) and FBs (weak binding mean: 58.7%; strong binding mean: 32.3%), orthogonal contrasts indicated that the anti-mycotoxin agent was able to effectively bind these toxins using Dose 1 (<i>p</i> < 0.05). This finding suggests that Dose 1 may be sufficient to achieve the targeted effect and that a further increase does not significantly improve the outcome. Regarding DON, a strong linear relationship was observed between dose and adsorption. However, the complex interactions between the mycotoxin, the ruminal environment, and the anti-mycotoxin agent made it difficult to establish a clear dose–effect relationship (<i>p</i> > 0.10). UHPLC-HRMS analysis identified over 1500 mass features in rumen samples, which were further analyzed to assess the effects of the anti-mycotoxin agent. Hierarchical clustering analysis (HCA) revealed significant changes in the untargeted metabolomic profiles of samples treated with mycotoxins compared to control samples, particularly after 24 h with the anti-mycotoxin treatments. Clear differences were noted between strong binding and weak binding samples. Further analysis using orthogonal partial least squares discriminant analysis (OPLS-DA) highlighted distinct metabolomic profiles, with stronger predictive ability in the strong binding group (Q<sup>2</sup> cumulative value of 0.57) compared to the weak binding group (0.30). The analysis identified 44 discriminant compounds in the strong binding model and 16 in the weak binding model. Seven compounds were common to both groups, while silibinin, known for its antioxidant and anti-inflammatory properties, was found among the unique compounds in the weak binding group. Overall, the findings suggest that both doses of the anti-mycotoxin agent significantly influenced the chemical profiles in the rumen, particularly enhancing the binding of mycotoxins, thereby supporting the role of phytogenic extracts in mitigating mycotoxin effects.
Oluwaseyi Oladipupo Jesusanmi, Amany Azevedo Amin, Norbert Domcsek
et al.
Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN–a library for building GPU accelerated spiking neural networks–we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model’s Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.
Tejal Vishandas Ahuja, Nidhi Bhatnagar, Shital Soni
et al.
Hemoglobinopathies are the most common hereditary disorders in India and pose a major health problem. Both beta-thalassemia and structural hemoglobin (Hb) variants are relatively common in North-Western India. Here, we report a case of a 26-year-old female (caste-Lohana) who came to us for premarital screening hemoglobinopathy. A complete blood count was done on automated cell counter. Hb analysis was carried out using high-performance liquid chromatography (HPLC) Bio-Rad VARIANT II Hb Testing System. HPLC analysis showed a peak in the unknown window with retention time (RT): 4.72 min and area: 18.9% and S-window with RT: 4.33 min and area: 0.5%, which was suggestive of Hb Q India. Further workup was done on other family members also. And found that the mother and sister of the patient also had similar findings (Hb Q India) and the father of the patient was positive for beta-thalassemia trait. Hb Q India is a rare hemoglobinopathy, which presents in mostly heterozygous form. The inheritance of Hb Q India is autosomal dominant.