Growing obesity has been a worldwide issue for several decades. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be difficult. This paper presents the design and development of a smartphone-based diet-tracking application, Smart Diet Diary, to assist obese people as well as patients to manage their dietary intake for a healthier life. The proposed system uses deep learning to recognize a food item and calculate its nutritional value in terms of calorie count. The dataset used comprises 16,000 images of food items belonging to 14 different categories to train a multi-label classifier. We applied a pre-trained faster R-CNN model for classification and achieved an overall accuracy of approximately 80.1% and an average calorie computation within 10% of the real calorie value.

Short-reach transmission systems traditionally utilize intensity modulation (IM) at the transmitter and direct detection (DD) at the receiver due to their cost-effectiveness, small footprint, and low power consumption. However, with the exponential increase in bandwidth demand, coherent optical communication systems have become necessary for long-haul distances, requiring application-specific integrated circuits (ASIC) and advanced digital signal processing (DSP) algorithms coupled with high-speed digital-to-analog and analog-to-digital converters to achieve Tbit/s speeds. As coherent technology matures, it will eventually become feasible for short-reach transmission. In this context, self-coherent systems have emerged as an intermediary solution, offering advantages over traditional IM/DD systems. While comprehensive review studies exist on self-coherent transceivers, they do not cover recent advances in phase retrieval methods for short-reach optical communications. This review article highlights recent developments in cost-effective self-coherent detection for short-reach systems through comparing the benefits of single sideband (SSB) transmission and Kramers-Kronig detection to carrier-assisted phase retrieval, the Gerchberg-Saxton (GS) algorithm, and the transport of intensity equation (TIE) method.

To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg–Marquardt neural networks (LM-NNs). A unique hybrid nanoliquid comprising aluminum oxide, $A{l}_2{O}_3$, nanoparticles and copper, $Cu$, nanoparticles is specifically addressed. Through the application of similarity transformations, the mathematical model formulated in terms of partial differential equations (PDEs) is converted into ordinary differential equations (ODEs). The BVP4C method is employed to generate a dataset encompassing various TRHNF-DC scenarios by varying magnetic parameters and nanoparticles. Subsequently, the intelligent LM-NN solver is trained, tested, and validated to ascertain the TRHNF-DC solution under diverse conditions. The accuracy of the LM-NN approach in solving the TRHNF-DC model is verified through different analyses, demonstrating a high level of accuracy, with discrepancies ranging from ${10}^{-10}$ to ${10}^{-8}$ when compared with standard solutions. The efficacy of the framework is further underscored by the close agreement of recommended outcomes with reference solutions, thereby validating its integrity.

This study aimed to investigate the use of social robots as an interactive learning approach for treating children diagnosed with autism spectrum disorder (ASD). A review was conducted using the meta-analysis technique to compile pertinent research. An analysis was performed on the results of the online search process, which gathered information on pertinent research published until 31 January 2025, from three publication databases: IEEE Xplore, SCOPUS, and Google Scholar. One hundred and seven papers out of the 591 publications that were retrieved satisfied the previously established inclusion and exclusion criteria. Despite the differences in methodology and heterogeneity, the data were synthesized narratively. This review focuses on the various types of social robots used to treat ASD, as well as their communication mechanisms, development areas, target behaviors, challenges, and future directions. Both practitioners and seasoned researchers looking for a fresh approach to their next project will find this review a useful resource that offers broad summaries of state-of-the-art research in this field.

A novel, programmable, mode-locked fiber laser design is presented and numerically demonstrated. The laser programmability is enabled by an intracavity optical phase-only pulse shaper, which utilizes the same linearly chirped fiber Bragg grating (LC-FBG) from its two opposite ends to perform real-time optical Fourier transformation. A binary bit-pattern generator (BPG) operating at 20-Gb/s and producing a periodic sequence of 32 bits every 1.6 ns, is subsequently used to drive an optical phase modulator inside the laser cavity. Simulation results indicate stable programmable intensity profiles for each optimized user defined 32 code words. The laser operated in the self-similar mode-locking regime, enabling wave-breaking free operation. The programmable 32 bit code word targeting a specific intensity profile was determined using 100 generations of the genetic algorithm. The control of ultrashort pulse intensity profiles on the picosecond and femtosecond time scales is difficult. The process of stretching and compressing the pulse in the time domain allows for a slower BPG to impose a predefined phase modulation prior to pulse compression. This results in control over the fine features of the intensity profile of the compressed pulse on a picosecond or femtosecond time scale inside the laser cavity. The stability of the proposed scheme depends on the consistency and accuracy of the BPG rise and fall times in practice.