Hydrodynamic cavitation-assisted tissue ablation using a continuum robotic device with heat and mass transfer considerations

Precision and efficiency constitute the main challenges in minimally invasive surgeries. Current endoscopic robotic system limitations have been sought for innovative design and optimization strategies. Moreover, the use of flexible surgical robots in high-speed flows such as cavitation flows is a problem that needs to be solved. To address these issues, the design optimization of a tendon-driven endoscopic robot based on hydrodynamic cavitation is presented in this study. The flexible part of the robot was analyzed using the Finite Element Method (FEM), where fluid–structure interactions and material mechanical properties were considered which led to the fabrication of a prototype. Employing the shadow-graphy technique, sprays emerging from the endoscopic robot were imaged at various bending angles and fluid pressures to examine carefully flow cone angle change effects. Subsequently, the results from the experiments involving ex vivo human cervix and uterus myoma tissues were included. Thermal conditions and mass transfer rates were measured according to variations in applied pressure, bending angle enabling quantitative assessment of heat diffusion and material removal efficiency at the tissue interface. This study focuses on the continuum endoscopic robots operating at high flow rates and presents an optimal working area via a prototype. Tissue experiments demonstrated that superficial endometrial ablation increased proportionally with the bending angle of the robot in both cervix and myoma tissues, which are characterized by their dense and resilient structure. The results highlight the interconnected dynamics of cone angle, flow pressure, and tissue ablation during robot bending. In short, this study presents guidelines for future studies on the design optimization of continuum robots for high-flow rate applications

Boosting supercapacitor performance via flower-like Cu₂XSnS₄ (X = Mn, Co, Ni, Ba, Zn) chalcogenides: solvothermal synthesis and charge storage behavior

In the present work, a series of Cu₂XSnS₄ (X = Mn, Co, Ni, Zn, Ba) quaternary chalcogenide phases were synthesized by a simple solvothermal method and evaluated as electrode materials in symmetric two-electrode supercapacitor cells. Structural and morphological analyses demonstrated the formation of flower-like crystalline structures, with significant variations in texture and surface area depending on the substituted metal. Among the synthesized samples, the Mn-substituted CMnTS electrode exhibited the highest specific capacitance of 386.0 F/g at a current density of 1.0 A/g and maintained 92 % of its initial capacity after 10,000 charge-discharge cycles. Electrochemical tests revealed that CMnTS stored charge predominantly through capacitive mechanisms, while the Zn-substituted sample exhibited diffusion-limited behavior. EPR analysis indicated the presence of paramagnetic species in CMnTS, which may contribute to its electrochemical performance. These findings highlight the critical role of metal substitution in tailoring the charge storage characteristics of quaternary chalcogenides and provide a promising approach for optimizing electrode materials

Validation of a lipopeptide approach to a safe-and-sustainable-by-design strategy on TiO2 nanoparticles UV filters

Titanium dioxide nanoparticles (TiO2 NPs) are well suited for cosmetics and polymer films because they efficiently absorb UV light while remaining transparent to visible light. Their widespread use requires strategies for managing potential human and environmental risks. Implementing the Safe and Sustainable by Design (SSbD) methodology to advanced chemicals and materials is a major global challenge and a concept that is included in several EU research projects. This study employed a SSbD strategy by functionalizing the surface of TiO2 nanoparticles with a lipopeptide-based biosurfactant (Sodium Surfactin, SS). A colloidal heterocoagulation approach was used to produce SS-modified TiO2 nanoparticles. Different design options (TiO2 source, order of addition, TiO2/SS weight ratio) were investigated, and the properties were compared by measuring the UV filtering capability, photoreactivity, dustiness index, biological and ecotoxicological endpoints. This allowed us to estimate the safety and sustainability profile in agreement with the steps suggested by the JRC SSbD framework. The lipopeptide-based coating was essential for managing UV light-induced photoactivity and significantly lowering both in vitro cytotoxicity and ecotoxicity while simultaneously enhancing photostability when applied in cosmetic formulations. These results demonstrate that a colloidal process, which can be easily scaled up for industrial purposes, is a promising and exploitable SSbD strategy for the design and implementation of TiO2 NPs based UV filters

Cellulose based pH-responsive smart membrane for selective removal of oil and water

Background: Removal of oil and organic compounds from industrial wastewater is of great importance to environmental pollution control. However, efficient, simple and cost-effective separation of such pollutants from water is still challenging. Methods: A cellulose-based pH-responsive smart membrane with excellent reversible wettability was developed. Cellulose membrane was coated with polydopamine (PDA) to introduce the reactive sites for further modification. Dimethylaminopropylamine (DMAPA) and octadecylamine (ODA) were selected as pH-responsive and hydrophobic materials, respectively. Significant findings: The modified cellulose membrane switches surface wettability between hydrophobic under neutral condition and hydrophilic in acidic environment (pH 2). Therefore, this cellulose membrane has great applicability to selectively remove oil or water according to the pH of water. The membrane exhibited high separation efficiency of 99 % in both cases. The membrane also absorbed the oil in water and desorbed it while immersing in acid (pH 2) presenting the “self-cleaning” ability. Such a smart membrane has significant potential in oil-water separation

A note on battery swapping policies in the electric vehicle routing problem with time windows and battery swapping vehicles

Çatay and Sadati [An improved matheuristic for solving the electric vehicle routing problem with time windows and synchronized mobile charging/battery swapping. Computers & Operations Research 159, 106310, 2023] explores a variant of the Electric Vehicle Routing Problem with Time Windows that incorporates mobile chargers for recharging electric vehicles (EVs) at selected locations while serving customers. The authors propose a matheuristic method to address this problem and its special case, where EV batteries are swapped in constant time instead of being recharged over variable durations. While comparing their results with those in the literature, the authors overlook a critical assumption regarding the swapping policy, potentially causing confusion in interpreting the findings. This note addresses the issue, clarifies the overlooked assumption, and updates the results that do not align with the actual scenario in the literature. Furthermore, it introduces two new battery swapping policies and presents an extensive computational study to offer new insights on synchronized mobile battery swapping

AdLU: adaptive double parametric activation functions

Activation functions are critical components of neural networks, introducing the necessary nonlinearity for learning complex data relationships. While widely used functions such as ReLU and its variants have demonstrated notable success, they still suffer from limitations such as vanishing gradients, dead neurons, and limited adaptability at various degrees. This paper proposes two novel differentiable double-parameter activation functions (AdLU1 and AdLU2) designed to address these challenges. They incorporate tunable parameters to optimize gradient flow and enhance adaptability. Evaluations on benchmark datasets, MNIST, FMNIST, USPS, and CIFAR-10, using ResNet-18 and ResNet-50 architectures, demonstrate that the proposed functions consistently achieve high classification accuracy. Notably, AdLU1 improves accuracy by up to 5.5 % compared to ReLU, particularly in deeper architectures and more complex datasets. While introducing some computational overhead, their performance gains establish them as competitive alternatives to both traditional and modern activation functions

A two-wave death story: fentanyl overdoses in the US, bullets in Mexico

We establish a link between the fentanyl crisis in the US starting in 2013 and a second wave of drug-related violence in Mexico. We argue that the demand for fentanyl from the US pushed emerging Pacific-based Mexican drug trafficking organizations to reoptimize their trafficking routes, leading to new clashes and violence, often in locations barely affected by the first wave of violence caused by the 2007 Mexican War on Drugs. Exploiting the differential impact that the Mexican War on Drugs and the demand of fentanyl had on different municipalities, we estimate that fentanyl caused about 20 additional homicides per 100,000 inhabitants