6746 research outputs found
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Use of Cspbx3 Nanocrystals as an Additive in Organic Solar Cell for Efficiency Improvement
No full textHere, we aimed to investigate the dependence of organic solar cells (OSCs) performance on the CsPbX3 perovskites nanocrystals (PNCs). The effect of presence of CsPbX3 (X = Cl, Br, and I) NCs in photoactive layer was studied using various characterization techniques. UV-Vis spectroscopy and XRD patterns of P3HT:PCBM thin films showed that the light harvesting, crystallinity and phase separation increased by the introducing PNCs, which improved the electrical properties. The AFM images supported the improved crystallinity of P3HT and homogeneous structure of photoactive layer. OSCs with the configuration of ITO/PEDOT:PSS/P3HT: PCBM: PNCs/LiF/Al were fabricated. The PNCs incorporation resulted to efficiency enhancement from 1.75 % to 2.88 % (P3HT:PCBM - CsPbBr3). The increase in efficiency was strongly related to the improved current density (J(sc)) (10.75 mA cm(- 2)) and open-circuit voltage (V-oc) (0.598 V).This project was funded by Konya Technical University Scientific Research Council under grant no. (232216040) . Therefore, the authors would like to thank to their technical and financial support.Konya Technical University Scientific Research Council [232216040
The Generalized One-To Pickup and Delivery Vehicle Routing Problem and Solution Methods
No full textThe Vehicle Routing Problem (VRP), one of the most important problems in the logistics industry, aims to find the most suitable routes for vehicles that meet the demands of customers. Many studies on VRP are presented in favor of this purpose, and solution approaches are obtained. VRP is examined under various variants, and one of these variants, the Pickup and Delivery Vehicle Routing Problem (PDVRP), deals with the pickup demands as well as the delivery demands of the customers. This problem is divided into three main categories according to demand type and route structure: one-to-many-to-one problems, many-to-many problems, and one-to-one problems. In one-to-many-to-one problems, there are goods at the depot to be delivered to customers as well as goods at customers to be carried back to the depot. In many-to-many problems, one node can be the origin or destination point for a good, and multiple nodes can be the origin or destination point for each good, while in one-to-one problems, there is a single origin and a single destination point for each unique demand. Within the scope of this thesis, a literature review is presented on the PDVRP. Then a problem that can be classified as one-to-one problems, has a practical equivalent, and has not been addressed in literature so far, is defined. A mathematical model is developed for the solution of the problem, and the model is improved using valid inequalities to improve the performance. Experimental studies are performed with GAMS/CPLEX on 270 randomly generated test instances. Since PDVRP is an NP-Hard problem like classical VRP, the mathematical model provides optimal solutions only for small-sized instances.Günümüzde lojistik sektörünün en önemli problemlerinden biri olan Araç Rotalama Problemi (ARP), müşterilerin taleplerinin karşılandığı araçlar için en uygun rotayı bulmayı amaçlamaktadır. Bu amaç kapsamında ARP üzerine birçok çalışma ortaya konmakta ve çözüm yöntemleri elde edilmektedir. ARP'nin en basit hali olan klasik ARP'de her müşteriye yalnızca bir kez uğranması, rotaların depoda başlayıp depoda sonlanması ve araçların belirli bir kapasiteye sahip olması gibi temel kısıtlar bulunmaktadır. ARP çeşitli alt başlıklarda incelenmekte olup bu başlıklardan biri olan Topla-Dağıt Araç Rotalama Problemi'nde (TDARP), klasik ARP'den farklı olarak müşterilerin dağıtım taleplerinin yanı sıra toplama talepleri de ele alınmaktadır. Bu problem, talep tipi ve rota yapısına göre; Birden çoğa-çoktan bire TDARP (one-to-many-to-one problems), Çoktan çoğa TDARP (many-to-many problems) ve Birebir TDARP (one-to-one problems) olarak üç ana kategoriye ayrılmaktadır. Birden çoğa-çoktan bire TDARP'lerde, depodan müşterilere teslim edilecek taleplerin yanı sıra müşterilerden de depoya geri taşınacak talepler bulunmaktadır. Çoktan çoğa TDARP'lerde, bir nokta birden fazla talep için başlangıç veya varış noktası olabilmekte iken Birebir TDARP'lerde, her talep için tek bir başlangıç ve tek bir varış noktası vardır. Bu tez kapsamında, ARP'nin türlerinden biri olan TDARP hakkında bir literatür taraması gerçekleştirilmekte olup Birebir TDARP kapsamında sınıflandırılabilecek, pratik hayatta karşılığı bulunan ve literatürde şimdiye kadar ele alınmamış bir problemin tanımı yapılmıştır. Problemin çözümü için bir matematiksel model geliştirilmiş olup matematiksel modelin performansını artırmak amacıyla modele geçerli eşitsizlikler eklenmiştir. Deneysel çalışmalar, rassal oluşturulmuş 270 adet test örneği üzerinde GAMS/CPLEX paket programı kullanılarak gerçekleştirilmiştir. Klasik ARP gibi TDARP de NP-Zor problemler sınıfına girdiği için matematiksel model ile yalnızca küçük boyutlu problemlerde optimal sonuçlara ulaşılmıştır
Error Performance of Df Cooperative Smts With I/Q Imbalance Over Beckmann Fading Channels
No full textThe direct down-conversion principle, which has generally been used in the design of multiple-input multiple- output (MIMO) schemes, including space modulation techniques (SMTs), is attractive to researchers because of its low cost, low power consumption with fewer components, flexible and simple structure. However, hardware imperfections such as in-phase (I) and quadrature-phase (Q) imbalance (IQI) negatively affect the performance of the systems with direct down-conversion in practice. On the other hand, cooperative communication is a promising technology that can be utilized in the design of future wireless networks due to its significant advantages such as increasing system reliability, extending network coverage, reducing channel degradation, and providing high quality of service. In this study, SMT-based methods are integrated into cooperative systems, and a flexible and comprehensive model is presented that is applicable to many channel structures. Specifically, the error performance analysis of space shift keying (SSK), spatial modulation (SM), and quadrature SM (QSM) systems in the presence of IQI in decode-and-forward (DF) cooperative communication is carried out by analytical derivations and computer simulations over generalized Beckmann fading channels. The obtained results show that the performance of SMT-based DF cooperative systems is superior to the conventional schemes, and the effects of receiver IQI can be eliminated by optimal detector designs.This work is partly based on the doctorate dissertation of the first author [27], and it was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214 International Doctoral Research Fellowship Programme.Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214 International Doctoral Research Fellowship Programm
Joint Vehicle and Courier Routing Problem in Last Mile Delivery With Parcel Lockers
No full textThis paper presents the joint vehicle and courier routing problem in last mile delivery using parcel lockers, which serve as both transshipment stations for crowd shipping and pick up locations for customers to self-pick up their orders. In the problem under study, any customer prefers to be a courier in return for a certain amount of compensation and to serve other customers along with himself. The objective of the problem is to minimize the sum of total travel costs arising from vehicles, total tardiness costs, and total compensation costs paid for couriers. A novel Mixed Integer Programming (MIP) model is developed, and comprehensive computational experiments are conducted on newly generated test instances. According to the results, CPLEX provides acceptable solutions only for small-sized instances, as it is rather sensitive to the number of customers and lockers in terms of solution time and quality. It is also observed that the performance of CPLEX deteriorates dramatically as the problem size increases, and it suffers to find any upper bound even for medium-sized instances within 3 h. So, we offer two metaheuristic algorithms for practical-sized cases: the Memetic Algorithm (MA) and Simulated Annealing (SA). Computational experiments demonstrate that both metaheuristics reach optimal solutions in a reasonable amount of time in all instances whose optimality is known. For medium-sized instances, although objective function values are close to each other, in 643 out of 864 medium-sized instances, MA provides superior results as compared to SA. Finally, for large-sized instances, both algorithms are run for both 180 and 360 s. According to computational experiments, the performance of SA in 180 and 360 s is found to be almost the same, as it shows fast convergence. While SA is more successful than MA within 180 s, both algorithms show similar performances in terms of the average objective function value for large-sized instances in 360 s
High-Performance Sodium Metaborate-Based Sers Substrates for Attomolar Detection
No full textAyhan, Muhammed Emre/0000-0003-2324-6858Silver nanoparticles (AgNps) supported sodium metaborate (SMB)-based substrates were developed as low-cost and robust platforms for surface enhanced Raman spectroscopy (SERS) applications. The substrates were fabricated with a simple nanostructure that ensured uniform distribution of AgNps on the SMB matrix. The SMB effectively modulated the physicochemical properties of the substrates and provided uniformity of the nanoparticles. Field emission scanning electron microscope (FESEM) analysis revealed a rough, uniform and thin substrate surface, which favored the adhesion of AgNps and the formation of hotspots. Energy dispersive X-ray (EDX) analysis confirmed the elemental composition, with no evidence of unwanted chemical interactions between the AgNps and the SMB matrix. The SERS performance of the composite substrate was evaluated using two different probe molecules, Rhodamine 6 G (R6G) and crystal violet (CV). The prepared AgNps/SMB4L@SiO2 SERS substrate showed high sensitivity with a detection limit of 0.1aM and a high enhancement factor (EF) of 2.55 x 1011 and 6.37 x 1011 for R6G and CV, respectively. SERS studies revealed that a synergistic effect was achieved with the combination of SMB and AgNps and the developed substrates exhibited excellent SERS performance. The simple and environmentally friendly manufacturing process and low limit of detection (LOD) make these SERS substrates an attractive alternative to conventional SERS substrates for applications in the field of trace detection of analytes.This research has been supported by the Scientific Research Projects Coordination Unit at Necmettin Erbakan University with Grant No: 24GAP19001.Scientific Research Projects Coordination Unit at Necmettin Erbakan University [24GAP19001
Analysis and Improvement Model of Security Hardening in Windows and Gnu Linux Operating Systems
No full textThe rapid advancements in the field of information technology have led to the diversification and proliferation of cyberattacks. As the number of security vulnerabilities increases and vulnerable systems become primary targets, the risks encountered in digital environments have grown significantly. Consequently, the security of operating systems, which constitute the backbone of information systems, has gained substantial importance. This thesis aims to conduct an in-depth analysis of the security risks associated with Windows and GNU/Linux operating systems and to develop innovative and systematic strategies to enhance their security. Automated solutions have been devised to improve security levels, focusing on fundamental security measures such as user authorization, disabling unnecessary services, network security, and software updates. The study adopts a harmonized approach based on the Information and Communication Security Guide - Bilgi ve İletişim Güvenliği Rehberi (BİGR) issued by the Republic of Turkey Digital Transformation Office, as well as international security standards, including CIS Benchmarks, ISO 27001, and NIST SP 800-53. By aiming to enhance the effectiveness of security management systems both theoretically and practically, this research seeks to strengthen the security of operating systems and establish a robust defense mechanism against contemporary cyber threats. The proposed improvement model categorizes recommendations into three groups based on the criticality levels outlined in the Information and Communication Security Guide - Bilgi ve İletişim Güvenliği Rehberi (BİGR). The findings indicate that the suggested model contributes to the development of more resilient systems in the field of cybersecurity by minimizing security risks and addressing findings identified during audits.Günümüzde bilişim dünyasındaki hızlı gelişmeler, siber saldırıların çeşitlenmesine ve artmasına yol açmıştır. Güvenlik açıklarının çoğalması ve savunmasız sistemlerin hedef haline gelmesiyle birlikte dijital ortamda karşılaşılan riskler artış göstermiştir. Bu sebep ile bilişim sistemlerinin temel yapı taşı olan işletim sistemlerinin güvenliği önemli derecede değer kazanmıştır. Bu tez, Windows ve GNU/Linux işletim sistemlerinin güvenlik risklerini derinlemesine inceleyerek, bu sistemlerin güvenliğini artırmaya yönelik yenilikçi ve sistematik stratejiler geliştirmeyi amaçlamaktadır. Özellikle güvenlik seviyelerinin yükseltilmesi için otomatize çözümler üreterek, kullanıcı yetkilendirme, gereksiz servislerin kapatılması, ağ güvenliği ve yazılım güncellemeleri gibi temel güvenlik önlemleri kapsamında iyileştirmeler yapılmıştır. Çalışmada, Türkiye Cumhuriyeti Dijital Dönüşüm Ofisi'nin Bilgi ve İletişim Güvenliği Rehberi (BİGR) ile birlikte uluslararası güvenlik standartları (CIS Benchmarks, ISO 27001, NIST SP 800-53 gibi) esas alınarak uyumlu bir yaklaşım benimsenmiştir. Hem teorik hem de uygulamalı bir şekilde güvenlik yönetim sistemlerinin etkinliğini artırmayı hedefleyen bu çalışma, işletim sistemlerinin güvenliğini güçlendirerek modern siber tehditlere karşı sağlam bir savunma mekanizması oluşturmayı amaçlamaktadır. Geliştirilen iyileştirme modeli, Bilgi ve İletişim Güvenliği Rehberi (BİGR) içerisindeki kritiklik seviyelerine göre 3 gruba ayrılmıştır. Önerilen iyileştirme modelinin, siber güvenlik alanında daha dayanıklı sistemler geliştirilmesine katkı sağlayarak, güvenlik risklerini minimize ettiği ve denetim sırasında tespit edilen bulguları kapattığı görülmüştür
Electrochemical Detection of Nucleic Acids Using Three-Dimensional Graphene Screen-Printed Electrodes
No full textElectrochemical approaches, along with miniaturization of electrodes, are increasingly being employed to detect and quantify nucleic acid biomarkers. Miniaturization of the electrodes is achieved through the use of screen-printed electrodes (SPEs), which consist of one to a few dozen sets of electrodes, or by utilizing printed circuit boards. Electrode materials used in SPEs include glassy carbon (Chiang H-C, Wang Y, Zhang Q, Levon K, Biosensors (Basel) 9:2-11, 2019), platinum, carbon, and graphene (Cheng FF, He TT, Miao HT, Shi JJ, Jiang LP, Zhu JJ, ACS Appl Mater Interfaces 7:2979-2985, 2015). There are numerous modifications to the electrode surfaces as well (Cheng FF, He TT, Miao HT, Shi JJ, Jiang LP, Zhu JJ, ACS Appl Mater Interfaces 7:2979-2985, 2015). These approaches offer distinct advantages, primarily due to their demonstrated superior limit of detection without amplification. Using the SPEs and potentiostats, we can detect cells, proteins, DNA, and RNA concentrations in the nanomolar (nM) to attomolar (aM) range. The focus of this chapter is to describe the basic approach adopted for the use of SPEs for nucleic acid measurement. © 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
A Hyperaccurate Semi-Analytical Method With Error Bound Analysis for Treating Fractional Integral Equations With Functional Kernels and Variable Delays
No full textThis study is concerned with treating the fractional integral equations with functional kernels and variable delays, introducing a hyperaccurate semi-analytical method based on the Stieltjes-Wigert polynomials, matrix expansions, and the Laplace transform. After analytically converting the terms in the governing equation into the matrix expansions of the Stieltjes-Wigert polynomials type at the collocation points, the method gathers these matrices into a unique matrix equation and then readily solves it by an elimination technique. The residual improvement technique is also introduced to correct the obtained solutions. The residual error bound analysis is theoretically proved via algebraical properties and the mean value theorem for fractional integral calculus, respectively. Six model equations are treated via the method, which runs on a devised computer program. Based on the outcomes, the method is straightforward to treat model equations and to encode its mainframe on a mathematical software
Identification of Low-Momentum Muons in the CMS Detector Using Multivariate Techniques in Proton-Proton Collisions at √s=13.6 TeV
No full textSoft muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb(-1).We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.). Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Science Committee, project no. 22rl-037 (Armenia); the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the "Excellence of Science -EOS" -be.h project n. 30820817; the Be.ing Municipal Science ; Technology Commission, No. Z191100007219010 and Fundamental Research Funds for the Central Universities (China); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Shota Rustaveli National Science Foundation, grant FR-22-985 (Georgia); the Deutsche Forschungsgemeinschaft (DFG), among others, under Germany's Excellence Strategy -EXC 2121 "Quantum Universe" -390833306, and under project number 400140256 -GRK2497; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Hungarian Academy of Sciences, the New National Excellence Program -UNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64, and 2021-4.1.2-NEMZ_KI-2024-00036 (Hungary); the Council of Science and Industrial Research, India; ICSC -National Research Center for High Performance Computing, Big Data and Quantum Computing and FAIR -Future Artificial Intelligence Research, funded by the NextGenerationEU program (Italy); the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundacao para a Ciencia e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF "a way of making Europe", and the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation, grant B39G670016 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (U.S.A.).FWF; FNRS; FWO (Belgium); CNPq; CAPES; FAPERJ; FAPERGS; FAPESP (Brazil); BNSF (Bulgaria); MoST; NSFC (China); CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG [MoER TK202]; Academy of Finland; MEC; CEA; CNRS/IN2P3 (France); SRNSF; BMBF; DFG; HGF (Germany); NKFIH (Hungary); DAE; DST; IPM; SFI (Ireland); INFN (Italy); NRF (Republic of Korea); MES (Latvia); MOE; UM (Malaysia); BUAP; CONACYT; UASLP-FAI (Mexico); PAEC (Pakistan); FCT (Portugal); MESTD (Serbia); PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); NSTDA; TUBITAK; DOE; NSF; Marie-Curie program; European Research Council; Horizon 2020 Grant [675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207]; COST Action [CA16108]; Leventis Foundation; Alfred P. Sloan Foundation; Alexander von Humboldt Foundation; Science Committee [22rl-037]; Belgian Federal Science Policy Office; Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); FWO (Belgium) under the "Excellence of Science -EOS [30820817]; Be.ing Municipal Science ; Technology Commission [Z191100007219010]; Fundamental Research Funds for the Central Universities (China); Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; Shota Rustaveli National Science Foundation [FR-22-985]; Deutsche Forschungsgemeinschaft (DFG) [EXC 2121, 400140256 -GRK2497]; Hellenic Foundation for Research and Innovation (HFRI) [2288]; Hungarian Academy of Sciences [K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, TKP2021-NKTA-64, 2021-4.1.2-NEMZ_KI-2024-00036]; Council of Science and Industrial Research, India - NextGenerationEU program (Italy); Latvian Council of Science; Ministry of Education and Science [2022/WK/14]; National Science Center [Opus 2021/41/B/ST2/01369, 2021/43/B/ST2/01552]; Fundacao para a Ciencia e a Tecnologia [CEECIND/01334/2018]; National Priorities Research Program by Qatar National Research Fund [MCIN/AEI/10.13039/501100011033]; ERDF "a way of making Europe" [MDM-2017-0765]; Programa Severo Ochoa del Principado de Asturias (Spain); National Science, Research and Innovation Fund via the Program Management Unit for Human Resources ; Institutional Development, Research and Innovation [B39G670016]; Kavli Foundation; Nvidia Corporation; SuperMicro Corporation; Welch Foundation [C-1845]; Weston Havens Foundation (U.S.A.
Ensemble-Based Hybrid Deep Learning for Monkeypox Detection: Merging Instance-Normalized Transformers With CNNs for Enhanced Diagnostic Precision
No full textMonkeypox has re-emerged as a global public health threat, particularly in regions lacking extensive laboratory infrastructure. To address the urgent need for rapid, non-invasive diagnosis, we introduce a hybrid deep-learning framework that fuses an instance-normalized vision transformer (IN-ViT) with ResNet-50. Our approach first applies instance normalization within each transformer encoder to stabilize per-patch feature statistics, then concatenates these global contextual embeddings with ResNet-50's locally extracted features via a lightweight multilayer perceptron. We evaluate performance on the publicly available Monkeypox Skin Lesion Dataset, comprising 3192 augmented images of monkeypox, chickenpox, and measles lesions, partitioned into 70% train, 10% validation, and 20% test sets. Against standalone baselines-VGG-16, VGG-19, ResNet-50, and a standard ViT-our IN-ViT + ResNet-50 ensemble achieves 96.26% accuracy, 96.35% precision, 96.26% recall, and 96.24% F1-score, representing a >= 1% improvement over prior state-of-the-art. Crucially, the model sustains real-time inference (similar to 30 ms per image on Tesla T4 GPU) and can be readily deployed in telemedicine or point-of-care screening. These results demonstrate that combining fine-grained instance normalization with feature-level fusion yields a robust and interpretable diagnostic tool. Future work will explore federated learning for cross-site generalization, advanced data-augmentation regimes to mitigate class imbalance, and clinical validation across diverse patient populations