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The combined inhibitory effect of butaselen and decitabine against lung cancer cells
Early levothyroxine sodium administration and clinical outcomes in patients with sepsis: a MIMIC-IV database analysis
Prioritization of waste management strategies in sugar industry using Fuzzy Analytic Hierarchy Process (FAHP): a sustainable approach
Association of thiamine supplementation with 30-day mortality among ICU patients with sepsis-associated delirium
Cyberchondria among college students and associated factors: a latent profile analysis
A comparative GWAS of eye colour in light and dark eye genetic backgrounds defined by HERC2 rs12913832 polymorphism in a Canadian cohort of European ancestry
A synergistic acid–base tandem co-sensitization approach using pyrimidine fluorescent dyes achieves 22% indoor efficiency
Abstract In this research, an integrated acid–base tandem co-sensitization approach was formulated to improve the photovoltaic efficiency of dye-sensitized solar cells (DSSCs) under both indoor and outdoor lighting conditions. Novel pyrimidine- fluorescent based organic co-sensitizers (AS-1–AS-4) were systematically combined with the Ru(II)-based N3 dye to investigate their synergistic effect on light harvesting, charge transfer, and long-term stability. The optimized tandem configuration (AS-1 (bottom) + N3 (top)) demonstrated a remarkable 68% improvement in overall efficiency compared to the single N3 reference cell, achieving 11.12% under AM 1.5G illumination and 22.02% under 1000 lx indoor light. This significant enhancement is attributed to complementary spectral absorption, efficient acid–base interfacial coupling, and suppressed charge recombination, as confirmed by UV–Vis, EIS, IPCE, and stability analyses. The findings demonstrate that the acid–base tandem co-sensitization technique offers a highly effective and promising route for developing next-generation dye-sensitized solar cells (DSSCs) with superior efficiency, stability, and performance under low-light conditions.
Effectiveness of conventional surface water treatment processes in reducing natural radionuclides in Nile River drinking water
Abstract Understanding the stage-specific performance of conventional water treatment processes in removing natural radionuclides is crucial for optimizing public health protection, particularly in regions dependent on major river systems like the Nile. This study comprehensively evaluates the effectiveness of each treatment stage in conventional water treatment plants across Upper Egypt in reducing natural radionuclides, radon-222 (Rn-222), radium-226 (Ra-226), radium-228 (Ra-228), and potassium-40 (K-40), in Nile River-derived drinking water. We collected 40 water samples from 10 representative Nile-fed treatment plants in Upper Egypt, at 4 key stages: raw water intake, post-coagulation/sedimentation, post-filtration, and final treated water. Rn-222 concentrations were measured using the RAD7 detection system with RAD H 2 O accessory, while gamma-emitting radionuclides (Ra-226, Ra-228, and K-40) were analyzed via NaI(Tl) gamma spectrometry after achieving secular equilibrium between each parent and its short-lived progeny. The multi-stage conventional treatment process demonstrated differential effectiveness across radionuclides through distinct removal mechanisms. The treatment sequence achieved cumulative removal efficiencies (R eff ) of 74.19% for Rn-222 through volatilization during various stages, 28.86% for Ra-226 through coagulation and particulate capture, 46.84% for Ra-228, and 20.17% for K-40, the lowest among the studied radionuclides, due to its predominantly dissolved ionic nature. Treatment stages contributed sequentially: coagulation removed 29.17% of Rn-222, 17.46% of Ra-226, 32.16% of Ra-228, and 11.85% of K-40; filtration further reduced Ra-226, Ra-228, and K-40, resulting in cumulative R eff values of 26.06%, 42.04%, and 12.84%, respectively, by the end of this stage, while for Rn-222, filtration significantly enhanced its removal to a cumulative R eff of 59.42%; final treatment (disinfection) achieved the aforementioned cumulative efficiencies for all radionuclides. The sequential multi-barrier approach resulted in calculated annual effective doses (D an ) of 18.8 µSv/year (adults), 28.3 µSv/year (children), and 15.4 µSv/year (infants), all well below the international screening level of 100 µSv/year for a single source of radiation in drinking water, applicable to all age groups. These processes effectively mitigate radiological risks, with filtration being particularly crucial for volatile radionuclides and coagulation-filtration being essential for radionuclides that are associated with suspended particles, such as Ra-228. These findings provide critical insights for water treatment optimization and regulatory compliance in river-dependent communities.
Atomistic and electronic insights into Ca2+ and Li+ intercalation in TiS2: a first-principles approach supported by electrochemical validation
Abstract Calcium-ion batteries are emerging as a sustainable and high-energy alternative to lithium systems, yet the atomic-scale origin of their ion–host interactions remains unclear. We clarified the coupling between ion mobility and electronic structure in titanium disulfide (TiS 2 ) by combining multiscale density functional theory with experimental analysis. Periodic VASP simulations and localized DV-Xα analyses revealed that Ca 2+ intercalation induces greater lattice expansion than Li + , lowers diffusion barriers, and enriches the density of states near the Fermi level, enhancing both ionic and electronic transport. Despite weaker Ca–S interactions, strong Ti–S covalency stabilizes the framework, yielding a theoretical open-circuit voltage of 1.383 V, which is lower than that of LiTiS 2 (1.948 V). Orbital overlap and charge-transfer analyses show that this lower voltage reflects a balance between multi-electron charge storage (z = 2 for Ca 2+ ) and moderated electronic restructuring, rather than a simple reduction in electrochemical performance. Electrochemical measurements confirm these results: Ca-intercalated TiS 2 delivers a first-cycle capacity of 201 mAh·g − 1 , superior diffusion coefficients, and 96.3% rate retention with stable cycling. This work provides the first atomistic evidence that Ca 2+ insertion facilitates ion transport while imparting structural resilience, offering a design framework for next-generation multivalent-ion batteries.
Ancient architecture image classification with progressive stacking pseudoinverse learning
Explainable AI for gastrointestinal lesion surveillance and precision targeted drug delivery
Abstract The Internet of Bio-NanoThings (IoBNT) promises revolutionary healthcare applications, particularly in targeted drug delivery. However, major challenges remain including safe nanodevice design, monitoring their behavior in biological environments and enabling reliable communication with external control systems. This work proposes an AI-assisted IoBNT architecture that combines gastrointestinal (GI) imaging with intelligent therapeutic supervision. A wireless ingestible imaging device (WIID) captures GI images, while an Artificial Intelligence Ciphered Link (AICL) analyzes them using convolutional neural networks (CNNs) trained with supervised contrastive learning and cost-sensitive fine-tuning. Unlike prior studies focused solely on tumors, our system is evaluated on the HyperKvasir dataset covering 25 GI disease classes, including neoplastic and inflammatory conditions. Explainable AI methods (GradCAM family) are employed with quantitative validation to improve model transparency. Drug transport and release are modeled using a multi-compartment pharmacokinetic framework with uncertainty analysis. Security protections using Quadratic Map Privacy Algorithm (QMPA), threat modeling and failsafe dosing limits are incorporated to enhance clinical safety. The system achieves 91.4% classification accuracy (weighted F1 = 0.91) on HyperKvasir, with stronger performance in neoplastic classes and lower accuracy in rare categories, emphasizing the importance of class-balanced evaluation. These results demonstrate the feasibility of integrating AI-based disease detection with controlled drug delivery, representing a step toward closed-loop, adaptive IoBNT therapeutics.
ROI-guided relational YOLO–SegNet transformer for lightweight bone tumor segmentation and classification from X-ray images
Multi-point collaborative mobile replica node detection protocol based on key negotiation
Abstract Replica node attacks, a common issue in wireless sensor networks (WSNs), can cause major damage. Most traditional replica node detection protocols are designed for static WSNs and are limited by easy information leakage, high storage and communication overhead, and a short network lifetime. Therefore, this study proposes a multi-point collaborative mobile replica node detection protocol based on key negotiation, referred to as KN-MCDP. The KN-MCDP scheme is designed for use in mobile wireless sensor networks (MWSNs) where a limited number of mobile nodes are deployed in static WSNs. The protocol can not only identify replica nodes in static WSNs but also determine whether a mobile node is a replica, thereby providing enhanced network protection. When cluster head nodes and mobile nodes communicate, they encrypt the exchanged information using digital signature technology, ternary symmetric polynomial technology, and symmetric encryption technology, thus preventing information leakage collected by cluster head nodes. Collecting network information using Bloom filters on cluster head nodes and mobile nodes reduces network storage and communication overhead. In different phases, the protocol employs cluster head nodes, mobile nodes, and the base station to identify and isolate replica nodes. This approach balances the energy overhead of the network and extends its lifetime. The experimental results demonstrate that the KN-MCDP protocol can achieve a high detection rate, reduce the network’s storage and communication overhead, balance energy overhead, and extend the network lifetime.
Metal-displacement-derived silver nanoparticles for visible-light catalysis and TENG-enabled circuit integration
Frontispiece: Bioinspired High‐Performance Neuromorphic Devices Enabled by Thienoviologen‐Based Electrochemical Ion Gating
Validation of the Persian version of the computer ergonomics knowledge assessment questionnaire among frequent computer users
Ghost-peak-based estimation of modulation amplitude in optical correlation-domain reflectometry
Bioinspired High‐Performance Neuromorphic Devices Enabled by Thienoviologen‐Based Electrochemical Ion Gating
Abstract Neuromorphic computing is a bioinspired paradigm that emulates the structure and functionality of biological neural networks, demanding cutting‐edge materials and device architectures. In this work, we present a bioinspired electrochemical neuromorphic device (BEND) utilizing a thienoviologen‐based electrolyte. The incorporation of thiophene groups into the viologen structure (ThV 2+ ) leads to a reduced energy gap, improved radical stability, and enhanced electrochemical activity. The device exhibits excellent ambient stability and continuously tunable conductivity in response to voltage pulse stimulation. When integrated into a convolutional neural network (CNN) for image recognition, BEND achieves an accuracy of nearly 80% on the Fashion‐MNIST dataset. Moreover, the device successfully mimics essential synaptic functions such as spike‐timing‐dependent plasticity (STDP), Pavlovian learning, and supports dual‐terminal logic gate operations. These results significantly expand the functional versatility of viologen‐based materials in neuromorphic electronics and offer new insights into the design of next‐generation electrochemical artificial synapses.