Kalyani Government Engineering College

The use of the oppositional crayfish optimization algorithm (OCOA) for the solution of electric vehicle charging station (EVCS) incorporation and network reconfiguration (NR) with distributed generation (DG) and capacitors placement problems in a radial distribution network (RDN), where active power loss and annual energy loss cost minimization are the main objectives of the study. An improved version of the crayfish optimization algorithm (COA) is created by adding oppositional behaviour into the primary COA algorithm for the generation of an opposite primary population to find the optimal solution. Two test networks (33-bus and 69-bus) are used to examine the effectiveness of the proposed OCOA algorithm with three different scenarios and they are (i) EVCS with unity power factor (UPF) DG and capacitor placement, (ii) EVCS with optimal power factor (OPF) DG and capacitor placement, and (iii) EVCS inclusion and network reconfiguration with optimal power factor (OPF) DG and capacitor placement. The OCOA method allows improvements of 81.45

Multimodal generative AI systems like Stable Diffusion, DALL-E, and MidJourney have fundamentally changed how synthetic images are created. These tools drive innovation but also enable the spread of misleading content, false information, and manipulated media. As generated images become harder to distinguish from photographs, detecting them has become an urgent priority. To combat this challenge, we release MS COCOAI, a novel dataset for AI generated image detection consisting of 96000 real and synthetic datapoints, built using the MS COCO dataset. To generate synthetic images, we use five generators: Stable Diffusion 3, Stable Diffusion 2.1, SDXL, DALL-E 3, and MidJourney v6. Based on the dataset, we propose two tasks: (1) classifying images as real or generated, and (2) identifying which model produced a given synthetic image. The dataset is available at https://huggingface.co/datasets/Rajarshi-Roy-research/Defactify_Image_Dataset.

This paper presents a new single-switch flyback converter with passive leakage recovery snubber comprising of three diodes, a clamp capacitor, and a small transformer, rated only 5

A flexible, intensity-modulated optical bend-sensor was realized using a TiO2 similar to gel-wax nanocomposite core and clear silicone-rubber cladding in a rectangular macro-waveguide format. A 940 nm infrared (IR) light emitting diode (LED) and Si PIN photodiode enabled simple photometric readout with electromagnetic-interference immunity in a thin, conformal package. An integrated analytical framework linked LED emission, coupling, wavelength-dependent absorption, macro-bending radiation loss, and photodiode responsivity to predict photocurrent versus bend radius; durability effects up to 10,000 cycles were captured by a polynomial update of a single core-dependent parameter. Fabrication employed cast silicone-rubber layers and a 0.25 wt% TiO2-loaded gel-wax core, with vacuum curing to improve optical quality. Electrical characterization established the LED operating point, and bending tests over the bend-radius (R) 1.0-75.0 cm validated the model with a maximum deviation within +/- 5 %. The practical linear range was R = 1-30 cm (full measurable range R = 1-75 cm), with model-to-measurement deviation within +/- 5 %; the pointwise sensitivity decreased monotonically from 120.7 to 34 mu A cm(-1) as R increased from tight to gentle bends. After durability testing, the straight-state current drifted modestly (approximate to 4.11 % at 5000 cycles; approximate to 14.33 % at 10,000 cycles), while the sensitivity remained nearly unchanged; dynamic response/recovery times were not measured in this quasi-static study. The architecture supports straightforward electronics, scalable geometry, and a pathway to mass production via 3-D printing, positioning the device for wearable joint-angle tracking, soft-robotic hinge feedback, field curvature monitoring, etc.

This research presents a novel wide-range planar-type programmable optically variable resistor (POVR) utilizing TiO2 similar to epoxy nanocomposites. Conventional variable resistors, such as digital potentiometers and motorized rheostats, either lack continuous resistance modulation or exhibit cumbersome structures. Recent advancements using optically coupled light emitting diode (LED) - light dependent resistor (LDR) configurations improved automation capabilities but had limitations due to circular cross-sectional designs. Here, the authors propose planar-type POVRs with rectangular cross-sectional optical fiber spacers (ROFS), suitable for integration into modern planar electronic circuits. These ROFS cores were fabricated from epoxy resin incorporated with varying concentrations of titanium dioxide (TiO2) nanoparticles (0.0, 0.125, and 0.25 wt%) to precisely control optical and electrical properties. The resistance modulation was achieved by adjusting the forward bias voltages of red-green-blue (RGB)-LED sources, enabling continuous resistance variation from hundreds of Ohms to tens of mega-Ohms. Comprehensive analytical modelling was developed and validated through extensive experimental measurements, showing a close match with an error margin below 4 %. Additionally, detailed characterization, including field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) analysis, and optical spectroscopy, confirmed uniform nanoparticle dispersion and favourable optical properties. The fabricated planar POVRs demonstrated excellent scalability, repeatability, and adaptability for advanced electronic applications.