Future Institute of Engineering and Management

Se alloying CdTe absorber brings a lot of potential to the solar cell industry. The optical properties and electronic structures of this CdTeSe/CdTe composite absorber solar cell are discussed in detail by simulation. The VOC and cell efficiency are increased when a fully interdiffused CdTeSe composite absorber layer (100 nm) gets used because of the substantial inner grain photoconductivity. The functionality of the solar cell is significantly impacted as the thickness of CdSe grows. It has been observed that a residual photoinactive CdSe layer negatively affects solar performance. The photoinactive part of the FTO/CdTeSe interface becomes the photoactive part when a very thin layer of CdS is inserted into the FTO/CdTeSe layer. Since the reduced bandgap of the CdTe1xSex layer reduces the solar cell VOC, a very thin CdS layer has been used between the FTO and the CdTe1-xSex layer to minimise the damage. In this case, the factors responsible for the highest ISC (0.363 Amp), VOC (0.6475), FF (78.8%) and conversion efficiency (18.39%) are discussed in the present study.

This paper presents a reversible, n‑bit Quantum Arithmetic Logic Unit (QALU) implemented in Qiskit that supports a classical ALU‑like instruction set: ADD, SUB, CMP, AND/OR/XOR and their negations, unary NOT, shifts and rotates, and operand passthrough. The QALU outputs a result register and status flags N, Z, C, V (negative, zero, carry/no‑borrow, signed overflow) and emits explicit comparison outputs EQ, LT u , LT s . The design is modular and operation‑selectable at compile time, enabling clean verification and resource accounting. To evaluate fault‑tolerant efficiency, the circuits are decomposed into a Clifford + T basis and measure T‑count alongside CX‑count and depth. Exhaustive verification on Aer (matrix‑product‑state simulation) confirms correctness for all input pairs for n = 4 across 16 operations. Hardware experiments on IBM Quantum (limited to n = 2) demonstrate end‑to‑end execution with dynamical decoupling and gate twirling, and readout mitigation via mthree. An analytical scaling discussion grounded in ripple‑carry adder theory and known Toffoli/T‑gate constructions is further provided. These results show that a practical, verifiable QALU with flags and compare can be implemented within Qiskit while exposing meaningful fault‑tolerant metrics and hardware‑realistic performance characterization. Key findings from the provided implementation artifacts: (1) Aer exhaustive verification (n = 4): the QALU was exhaustively tested over all 2 2n =256 input pairs for each of 16 operations (4096 total tests), with 0 mismatches against a classical reference model. (2) Resource metrics (n = 4): after transpilation to a Clifford + T‑style basis {cx, h, s, sdg, x, z, t, tdg} at optimization level 3, the highest‑cost operations (ADD, SUB, CMP) report T‑count = 86, CX‑count ≈ 102–106, and depth ≈ 138 on a 24‑qubit circuit instance (including flags and MCX ancillae). (3) IBM hardware test (n = 2): on an automatically selected IBM backend (ibm_torino) using dynamical decoupling and gate twirling plus mthree readout mitigation, an exhaustive run over all 256 circuits yielded raw correctness 91.41% and mitigated correctness 91.02% (slightly worse post‑mitigation, consistent with practical tradeoffs where mitigation noise/calibration drift can dominate at small sizes).

Hematopoietic stem and progenitor cells (HSPCs) sustain lifelong blood production, yet the molecular mechanisms underlying their functional decline with age remain incompletely understood. Understanding how aging alters the transcriptomic landscape of HSPCs is critical to uncovering the origins of immune system aging. We performed a comprehensive single-cell RNA sequencing analysis integrating over 300,000 bone marrow-derived HSPCs from 50 healthy individuals spanning 19 to 84 years of age. Aging was associated with immune lineage skewing, marked by increased myeloid and decreased lymphoid output in both bone marrow and peripheral blood. Subtle increases in HSCs, MEPs, and myeloid progenitors alongside reductions in lymphoid progenitors were already evident in aged bone marrow, suggesting that lineage bias is encoded at the progenitor level. Age-associated transcriptional changes included extensive upregulation of ribosomal genes encoding small (RPS11, RPS12, RPS23) and large (RPL9, RPL19, RPL24) cytoplasmic ribosomal subunit proteins, as well as pro-inflammatory mediators (IL1B, IL18, TGFB1, S100A8). Enrichment analysis identified mitochondrial function, ribosome biogenesis, chromatin remodeling, and inflammatory signaling as key ontologies disrupted during HSPC aging. Our study identifies molecular signatures of systemic aging rooted in bone marrow HSPCs and suggests that dysregulated ribosomal protein gene expression is an under-appreciated hallmark of hematopoietic stem cell aging.

An extensive comparative examination of Diabetic Retinopathy (DR) detection methods is presented in this paper, with a focus on examining the efficacy of deep learning methodologies and segmentation strategies, primarily Convolutional Neural Networks (CNNs) and Residual Networks (ResNets). The work starts with a thorough literature analysis that covers the most recent developments in deep learning architectures and conventional segmentation techniques. CNNs and ResNets are particularly well-suited to capturing complex retinal properties that suggest deep learning. Using publicly available retinal image datasets, a range of segmentation techniques, CNNs, and ResNets are carefully tested during the project’s implementation phase. Evaluation metrics are used to thoroughly evaluate each methodology’s performance, including sensitivity, specificity, accuracy, and other pertinent metrics. In addition, the research explores how the size, diversity, and picture quality of the datasets affect the effectiveness of the suggested techniques, offering insights into the findings’ generalizability and practical use. The results of this thorough comparison study are intended to provide detailed insights into the advantages and disadvantages of segmentation techniques, CNNs, and ResNets for DR detection. The paper’s results help practitioners and researchers create earlier diagnosis strategies for DR that are more successful. This work promotes the development of precise and understandable tools, which eventually improve the outcomes of diabetic eye care and open the door for improved patient management techniques.

An image caption generator is an AI- and machine-learning–based tool that automatically creates natural-language textual descriptions of visual content in images, enhancing accessibility for visually impairedindividuals and supporting applications like image retrieval. To address this, we introduce a novel methodfor generating image captions without the need for paired image-text datasets. Our system utilizes Con-volutional Neural Network (CNN)-based encoders, including VGG16, VGG19, VGG30 and InceptionV3to extract detailed image features. These features are then decoded using Long Short-Term Memory(LSTM) and Transformer models to generate captions. Instead of relying on supervised learning, themodel is trained with unpaired data and optimized through deep learning, focusing on language fluencyand semantic coherence. This unsupervised approach allows for more flexible and autonomous captiongeneration. Experimental results on benchmark datasets such as Flickr8k and Conceptual Captionsdemonstrate that our method achieves competitive performance, with BLEU scores of 0.53 (VGG16),0.54 (VGG19), 0.55 (VGG30), 0.42 (Transformer) and 0.72 (InceptionV3 with optimization). The modelconsistently produces fluent and contextually accurate captions, highlighting the effectiveness of vision-language alignment in an unsupervised framework. This research marks a significant step toward buildingscalable and autonomous image captioning systems.