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Silicon photonics has gained great success mainly due to the promise of realizing compact devices in high volume through the low-cost foundry model. It is burgeoning from laboratory research into commercial production endeavors such as datacom and telecom. However, it is unsuitable for some emerging applications which require coverage across the visible or mid infrared (mid-IR) wavelength bands. It is desirable to introduce other wideband materials through heterogeneous integration, while keeping the integration compatible with wafer-scale fabrication processes on silicon substrates. We discuss the properties of silicon-family materials including silicon, silicon nitride, and silica, and other non-group IV materials such as metal oxide, tantalum pentoxide, lithium niobate, aluminum nitride, gallium nitride, barium titanate, piezoelectric lead zirconate titanate, and 2D materials. Typical examples of devices using these materials on silicon platform are provided. We then introduce a general fabrication method and low-loss process treatment for photonic devices on the silicon platform. From an applications viewpoint, we focus on three new areas requiring integration: sensing, optical comb generation, and quantum information processing. Finally, we conclude with perspectives on how new materials and integration methods can address previously unattainable wavelength bands while maintaining the advantages of silicon, thus showing great potential for future widespread applications.

This study investigates the correlation between electronic waste recycling, material recovery, and the consequent purity levels. It underscores the substantial influence of hydrometallurgical processes on the recycling process. Experiment 2, with an optimum chemical concentration, exhibited a 50% increase in metal recovery efficiency relative to Experiment 1. This illustrates the substantial improvement in material purity attained by hydrometallurgical experiments. The analysis of contaminant removal demonstrates a 40% enhancement in separation efficiency in Experiment 2, signifying the process’s efficacy. Experiment 3, which included further optimization of chemical parameters and modifications to process temperature, achieved a 30% decrease in recycling time, underscoring enhanced operational efficiency. Notwithstanding the elevated beginning expenses of chemical reagents, Experiment 2 achieved a 20% decrease in total recycling costs, underscoring the economic feasibility of the method. This highlights the need of meticulously controlling electronic trash to get elevated material recovery rates and cost-effectiveness. The findings elucidate the interplay between hydrometallurgical methods and electronic waste recycling, presenting ideas for enhancing recycling efficiency and environmental sustainability.

Biodiesel manufacturing from waste cooking oil has emerged as a potential alternative in the search of sustainable energy. This process helps mitigate environmental pollution and reduces reliance on fossil fuels. This research examines the catalytic efficiency of environmentally friendly catalysts in this process, with a specific emphasis on catalysts based on enzymes. It assesses their effectiveness in terms of the production of biodiesel, the rate of the chemical reactions, cost efficiency, and their influence on the environment. Experimental evidence demonstrates that enzyme-based catalysts have enhanced catalytic activity, leading to an average biodiesel production of 90%, outperforming traditional catalysts such as solid acids, bases, and heterogeneous metal catalysts. Moreover, enzyme catalysts exhibit enhanced reaction rates due to their unique enzymatic activity and gentle reaction conditions. The cost study shows that the manufacturing costs for enzyme catalysts are competitive, with an average total cost of $800, which is equivalent to traditional catalysts. Environmental impact evaluation emphasizes the sustainability of enzyme catalysts by demonstrating their lower energy consumption, waste production, and greenhouse gas emissions compared to traditional alternatives. The results highlight the capacity of green catalysts, namely enzyme- based catalysts, to enhance sustainable biodiesel production methods, hence promoting a more eco-friendly and robust energy framework.

We explore the many ways biocompatible nanomaterials may be used in sustainable biomedical settings. Quantum dots are 10 nm in size, carbon nanotubes are 50 nm, iron oxide nanoparticles are 25 nm, gold nanoparticles are 20 nm, and silver nanoparticles are 30 nm. The physicochemical features of these nanomaterials are different from one another. These nanomaterials may encapsulate therapeutic substances, according to drug loading evaluations; for example, gold nanoparticles can hold 15 mg/g of iron oxide, 12 mg/g of silver, 18 mg/g of carbon nanotubes, 20 mg/g of carbon, and 10 mg/g of quantum dots. Nanoparticles of gold (95% vitality after 24 hours), silver (93% viability), iron oxide (97% viability), carbon nanotubes (92% viability), and quantum dots (90% viability) highlight the biocompatibility of these materials. Fluorescence intensities of 1000 AU for gold nanoparticles, 980 AU for silver nanoparticles, 1050 AU for iron oxide nanoparticles, 900 AU for carbon nanotubes, and 1100 AU for quantum dots were observed in in vivo imaging investigations, further demonstrating the potential of these nanomaterials as contrast agents. By conducting thorough assessments and analyses, this study reveals how biocompatible nanomaterials can be used to create long-term biomedical applications, such as molecular imaging and targeted drug delivery, which will improve healthcare solutions and patient outcomes.

A suspension bridge and a cable-stayed bridge are compared under various loads and environmental conditions in this study. Under live, dead, and seismic loads, the research investigates axial forces, displacements, bending moments, and shear forces using advanced analytical methods and software such as CsiBridge, SAP2000, and Staad Pro. Steel plays a key role in the construction of suspension bridges and cable-stayed bridges, as this study thoroughly compares both types of bridges. Csi Bridge software is utilized in this study to investigate the axial force, displacement, bending moment, and shear strength of these bridges under various loading conditions. Cable-stayed bridges are recognized for their aesthetic benefits and structural efficiency for medium-to-long spans, and suspension bridges for their high tensile strength and durable design. The two bridge types exhibit different shear forces, displacements, and torsional values, underscoring their unique engineering designs.

In SCC, also called self-compacting concrete (SCC), voids are filled by its own weight, which prevents vibration. Large office building construction in Japan can significantly reduce noise levels on construction sites and associated environmental effects by implementing the use of SCC. According to the method, gravel makes up 50% of the concrete mix's packed density, while sand makes up 50% of mortar's packed density. Because gravel and sand are considered independently, SCC has a relatively high paste content. Thus, many SCC mixes are stronger than they should be. Recently, there has been a greater acceptance of the use of SCC. Concrete is a crucial and often utilised building material, hence attempts must be made to make it better by, for instance, constructing structures with enhanced mechanical and durability properties. The behaviour of concrete’s compressive strength and its splitting tensile strength is correlated. Analysis and experimental testing of self-compacting concrete (SCC) from different concrete classes were conducted. It was expected that both types of concrete would reach their desired compressive strengths of 25 N/mm2, 30 N/mm2, and 35 N/mm2 at 7, 14, and 28 days. Concrete sample measuring 150 mm in cube shape had their compressive and splitting tensile properties evaluated at 7, 14- and 28-days using compression testing apparatus.

The challenges arising due to the construction industry in balancing the development along with ecological concerns are being focussed upon in this paper. The detailed analysis of the materials that are being brought to use as a sustainable material in the constructions sectors is being focussed upon. Since the late 20th century, there have been significant developments in the building industry. Since the population is rising and the need for housing is increasing, all available resources must be employed to satisfy the demand as it stands. This will have a significant long-term effect on the situation since the resources at hand are insufficient to meet the demand. There are many data and trends about sustainable construction approaches that is being developed by the researchers across the world that form the basis of this review study. The work found that associated industries should be encouraged to take part in the seminars, workshops, and other training programs that are organized at various events in order to deepen and improve their comprehension of the concept of sustainable development. The comparison drawn-out in between the conventional and a smart building concludes the benefits of using sustainable methods in the construction industry.

Environmental engineering is a broad field that considers the complex interactions between natural issues and human action to make long-term arrangements and provide solutions. Natural designing uses many principles from the field of chemistry science, material technology, and other research to analyse and carry out strategies for resource conservation, protecting the environment, and the minimization of contaminated particles and polluting substances. The focus of the paper is on the sustainable and waste management practices that can reduce the undesirable effects such as carbon prints on the environment. the framework has been designed to attract more and more industries and management to achieve the sustainable growth of the sector. The key objectives of remediation activities are to safeguard public health, repair harmed ecosystems, and encourage the long-term, sustainable use of contaminated land or that area. Environmental engineering will continue to be essential in helping to shape a more sustainable relationship between humans and the natural world as society faces constantly shifting ecological issues and all the challenges related to the environment.

The recent world is being focused on deriving methods for using renewable energy-based systems to meet the energy demands. There are various research areas to be focused upon for making the output from these energy systems more reliable and efficient. The focus of this paper is on examining on such key component, a Unified power flow Controller (UPFC) into solar Photovoltaic (PV) structures to enhance green energy transmission and efficiency. The UPFC structure has the benefits combining the STATCOM and SSSC, which is found to be vital in regulating the active and reactive power flows within the network, ensuring stability amidst system fluctuations. The software MATLAB/SIMULINK is being used for making a 400.0-kW PV-UPFC farm, the study explores the effect of UPFC on enhancing power quality and stability, addressing real-world challenges like harmonics and voltage fluctuations like sags/swells. The work also delves into novel control strategies, consisting of using Proportional-integral (PI) controllers and advanced optimization techniques, to manage the power flow effectively. It can be concluded that the research highlights the UPFC’s capability in mitigating issues inherent in integrating solar energy into the grid, inclusive of voltage rise, reverse power flow, and system instability, by means of showcasing more desirable system voltage profiles and decreased energy oscillations in the designed system.

One of the key elements of decarbonizing global energy networks and integrating renewable energy sources is green energy storage technology. Energy Storage Systems (ESS), which store surplus produced electricity and make it available on demand, are essential for reducing fluctuations. Electromechanical, electromagnetic, thermodynamic, chemical and hybrid approaches have all been used in the development of energy storage technologies. A comprehensive list of current papers in the literature section is compiled to illustrate the range of advancements in this field. This paper reviews green energy storage systems, focusing on their primary uses. Power utilities will benefit from this thorough analysis of energy storage systems; the researchers choose the finest and newest energy storage technology based on its practicality and affordability. These days, several nations use energy storage systems to plan for future energy needs. Variations in solar radiation cause a solar photovoltaic generator to overproduce electricity. The implementation of a hybrid energy storage system would help to increase the reliability of solar-powered power generation. The microgrid is a crucial component of the smart grid network for solar installations. This study looks at the microgrid's energy storage system for photovoltaic systems. The topologies and storage system configurations of the microgrid are analyzed together with power electronic interference, control systems, and optimization of the energy storage system and renewable sources. a general technique for sizing the HESS of PV systems using design space as well as pinch analysis. HESS scales that link generator ratings to storage capacity are developed in the proper sizes by using pinch analysis to load and resources data.

Reinforced refers to a structurally sound assemblage of carefully joined slabs, beams, columns, and foundation components. Through the use of this complex network, loads are systematically transferred from slabs to beams, then to columns, converge at the foundation, and finally travel to the soil beneath. This structural analysis offers a thorough investigation of load-carrying dynamics by examining multiple scenarios for the same structure while accounting for varying wind speeds. A G+9 storey building is subjected to a comparative evaluation in three different wind zones (I, II, and III) with corresponding wind speeds of 33 m/s, 39 m/s, and 44 m/s. The structural behaviour is carefully modelled and examined under the impact of dead load, live load, and wind load using sophisticated STAAD Pro software. This thorough analysis clarifies the structure’s unique reactions to different wind speeds. In order to determine the design loads of a multistorey building, this paper gives a comparative assessment of wind load. Then, using the fundamental wind speed and other local characteristics, the wind load in that zone may also be calculated. The wind speed is time-dependent and random, though. The current study uses the IS 875 code to analyse wind loads in different zones of a multistorey building. The design wind speed of that zone, with a variance, is used to estimate the wind loads.

This review paper explores the transformative role of big data analytics in geotechnical engineering, transferring past conventional methods to a data-driven paradigm that complements decision-making and precision in subsurface investigations. By integrating large statistics analytics with geotechnical engineering, this study demonstrates big improvements in website characterization, danger assessment, and production methodologies. The research underscores the capability of big data to revolutionize geotechnical investigations through improved prediction models, threat management, and sustainable engineering practices, highlighting the critical role of big data in addressing international warming and ozone depletion. Through the examination of numerous case studies and AI-driven methodologies, this paper sheds light at the efficiency gains and environmental benefits attainable in geotechnical engineering.

This paper presents a novel use of machine learning techniques for identifying faults in renewable microgrids within the field of decentralized energy systems. The study investigates the effectiveness of machine learning models in identifying abnormalities in dynamic and variable microgrid environments. It utilizes a comprehensive dataset that includes parameters such as solar, wind, and hydro power generation, energy storage status, and fault indicators. The investigation demonstrates a notable 94% precision in identifying faults, highlighting the superiority of machine learning compared to conventional rule-based approaches, which attained an accuracy rate of 80%. The precision and recall measures emphasize the well-balanced performance of the machine learning models, reducing both false positives and false negatives, and guaranteeing precise problem detection. The effect of faults on microgrid efficiency is significantly reduced, with an only 2% decrease recorded under fault situations, demonstrating the models’ ability to maintain an efficient energy supply. A comparative study reveals a 14% improvement in accuracy when compared to conventional techniques, emphasizing the benefits of adaptive and data-driven approaches in identifying intricate fault patterns. The sensitivity study validates the resilience of the machine learning models, demonstrating their capacity to adjust to different settings. The practical application of the models is validated by real-world testing in a simulated microgrid environment, which leads to their repeated improvement and improved performance. Ethical concerns play a crucial role in assuring ethical data use during research, particularly in the implementation of machine learning, by upholding privacy and security requirements. The study results indicate significant implications for identifying faults in renewable microgrids, providing a potential opportunity for the progress of robust and sustainable decentralized energy networks. The effectiveness of machine learning models stimulates further study in expanding their deployment for varied microgrid situations, including more machine learning approaches, and resolving obstacles associated with real-time application in operational settings.

As the healthcare landscape embraces the revolutionary power of digitalization, improving inventory systems has become a top priority. Hospital asset tracking primarily focuses on monitoring various assets within a healthcare facility. These assets encompass human resources, medical equipment, vehicles, and other essential resources crucial for seamless hospital operations. Tracking assets in a hospital setting can range from monitoring individual medical devices to overseeing entire fleets of equipment and staff resources. The central objective is to maintain real-time records of asset status, and location to optimize productivity while minimizing costs. This project tracks the assets within the hospital using RFID sensors and outside the hospital using GPS. Radio frequency identification system (RFID) is an automatic technology and is used to identify objects, and record data through radio waves. Hospital asset management and tracking has become one of the main priorities in healthcare as it saves hospitals both money and time. Hospitals are under constant pressure to persistently track their medical equipment and supplies to make sure that they are properly utilized. This enhances financial performance and ensures the delivery of high-quality patient care. These are the advanced approaches to improve hospital asset management through real-time tracking and data analysis.

This study presents a revolutionary approach to surface enhancement through microwave-assisted cladding of a Ni and 15% BN particle mixture onto SS-304 substrates. Meticulous preparation steps were undertaken, including substrate cleaning and powder preheating, to ensure optimal adhesion and coating quality. Microwave hybrid heating using charcoal as a susceptor material facilitated rapid and uniform heating of the powder mixture, while a pure graphite sheet prevented contamination during the process. The experiment was conducted using a multimode microwave applicator at specific power and frequency settings, resulting in controlled heating for optimal coating formation. The precision of the microwave-assisted cladding process was illustrated through SEM images, revealing a uniform distribution of cladding particles across the substrate. Moreover, significant improvements in surface hardness and wear resistance were observed, with a 44.67% increase in surface hardness and a low wear rate of 0.0020 mm3/m. These findings highlight the effectiveness of the developed cladding technique in enhancing the mechanical properties and wear resistance of SS-304 substrates, paving the way for its potential application in various industries requiring reliable surface protection and durability under sliding contact conditions.

This study focuses on enhancing surface protection through microwave-assisted metallic cladding of a Ni-ZrO2 mixture onto SS-304 substrates. Meticulous preparation steps ensured effective deposition: SS-304 underwent thorough cleaning, while the Ni-ZrO2 mixture was preheated to remove moisture. Maintaining uniform distribution was crucial for consistent coating thickness. Microwaves interacted differently with materials, necessitating consideration of material-specific skin depth to avoid direct particle-microwave interaction. Microwave hybrid heating (MHH) with charcoal as a susceptor overcame microwave reflection, facilitating rapid heating of the powder mixture. A pure graphite sheet prevented contamination during MHH. Subsequent irradiation in a multimode microwave applicator for 120 seconds ensured uniform and controlled heating, as illustrated in Figure 1. SEM analysis revealed the distribution and morphology of Ni and ZrO2 particles on the SS-304 surface, indicating effective deposition and adherence. A significant improvement in surface hardness (40.78%) post-cladding was observed. Furthermore, the wear rate and coefficient of friction of the cladded surface were determined to be 0.00124 mm3/m and 0.243, respectively. These findings highlight the promising tribological performance of the Ni-ZrO2 cladded surface, underscoring its potential for applications requiring enhanced surface protection and improved mechanical properties.

The work addresses the communication barriers experienced by semiparalyzed individuals through the introduction of the “paraglove,” a cuttingedge wearable device leveraging Internet of Things (IoT) technology. The paraglove is equipped with flex sensors, seamlessly integrated into the glove, and connected to an ESP32 microcontroller board. The flex sensors detect minimal hand gestures, allowing users to signal for crucial activities such as eating, restrooms, and signalling for emergencies. Through IoT, these gestures are translated into text messages, a process facilitated by the ESP32 microcontroller. The resulting text data is then stored and continuously updated within Google Firebase. To ensure seamless communication, the paraglove utilises the Kodular Companion application. The sustainable information is transmitted to family members or guardians through the application, enhancing the connectivity and support system for individuals with paralysis. The integration of an OLED display sensor directly on the glove provides users with real-time feedback by presenting the interpreted output. The combination of flex sensors, ESP32 microcontroller, IoT technology, Google Firebase, and Kodular Companion application simplifies communication for those facing paralysis and empowers them to engage with the world more effectively. This technological solution addresses the immediate communication needs and contributes to the overall well-being of individuals with paralysis.

The expanding population in the past decade has put an enormous strain on building materials sector, prompting civil engineers to find creative strategies for turning waste into viable assets. Employing such waste as an invaluable asset may contribute to financial savings on the disposal of waste, enhance safety and security for the public, encourage environmental awareness, and save restricted natural resources. The present paper is part of a larger analysis on the applications of bricks. The particular focus is on the use of different waste materials in the process of producing them. The mechanical and physical attributes of bricks provide recommendations for further study in this field. Further research is required for finding a cost-effective brick production method that utilizes less energy and generates a lesser number environmental pollutants. Brick kilns are asked to shift towards utilizing alternate fuels like methane gas or petroleum oil. Furthermore, research is required to determine how to fulfill the material criteria of the standard while safeguarding the environment, in particular view of a growing movement in the construction sector toward the use of low-cost, lightweight, and green construction supplies. Recycled waste materials from farms and factories can reduce environmental harm and fulfill the need for more economically efficient ways of building.

The development of machine learning (ML) methods in the field of material science has provided new possibilities for predictive modeling, especially in the field of mechanical material evaluation. The study provides an in-depth investigation of the utilization of various machine learning methods in predicting of mechanical characteristics throughout a range of different materials. A range of supervised learning models, such as regression tree models, support vector machine models, and neural networks, have been used to examine and forecast significant mechanical properties, including strength, ductility, and toughness. The models completed training as well as validation processes employing broad datasets obtained from experimental mechanical tests, covering tensile, compression, and fatigue examinations. Major focus was given to the process of choosing features and optimization in order to boost the accuracy and dependability of the predictions. This approach not only simplifies the method of material development but also improves understanding of the complex links among material composition, methods of processing, and mechanical properties. The research further examines the barriers and potential outcomes of applying machine learning (ML) in material characterization. It stresses the possibility for further improvements in predicted precision and efficiency of computing. Support vector machines, supervised artificial neural network, regression trees are most popular ML technique used in conducting predictive modelling.

This study examines the rapidly changing and competitive area of automobile design, with a particular focus on the introduction of autonomous vehicles. The study moves the focus of the conversation from conventional automotive design approaches to new, usercentric techniques which make use of the opportunities of autonomous technology. Moreover, it assesses the potential effects of different design choices on the experience of users, protection, and transportation within cities. Also, the research offers realistic viewpoints on the coming shifts and alterations in mobility design, predicting the significant effect of autonomous vehicles on both personal and social transportation models. When it comes to aesthetics, the recommendations for designing autonomous vehicles highlight finding an equilibrium between practicality and efficacy without compromising aesthetic appeal. Incorporating adaptable design characteristics that may be changed according to different user needs is advised, while also giving importance to ergonomics and simple designs that improve user interaction. Vehicle aerodynamics should be enhanced, and sustainable materials should be used if possible because these actions can help cut reduce emissions and energy use. In addition, the design process must take into account the integration of cutting-edge technology, including artificial intelligence and machine learning, to improve the vehicle's functionalities while verifying that aesthetic improvements have no impact on the vehicle's efficiency.

The technological development of biomaterials used in forming artificial organs and organoids indicates a revolutionary area within biomedical engineering and the field of regenerative medicine. This study provides an in-depth review of recent progress in biomaterials, emphasizing their design and use for fabricating artificial organs and organoids. The analysis proceeds with examining the necessary parameters for biomaterials in simulating the biological and biomechanical qualities of local tissues. The next effort turns towards synthesizing and characterizing innovative biomaterials, including biocompatible polymers, hydrogels, and bioactive scaffolds that can be tailored to suit specific organ systems. The paper provides an in-depth take on the developments in 3D biological printing and microfabrication techniques, emphasizing how they facilitate the synthesis of complicated, multicellular structures. The research also examines the integration of biomaterials when combined with stem cell technologies, focusing on their role in forming organs and the prospects for customized medical treatments. This review highlights the significant developments achieved in this area and the potential of these technologies in addressing the limited supply of organs, performing drug testing, and improving knowledge of the growth of organs and diseases.

Parking spots have become a prevalent concern in urban growth. The number of automobiles is increasing faster than accessible parking spaces. This problem is addressed by implementing an Internet of Things (IoT)-based Parking Surveillance Scheme (IoT-PSS) for the smart campus. The system aims to efficiently match vehicles with available parking spaces, resulting in time savings, improved parking space usage, reduced management expenses, and decreased traffic congestion. Edge computing provides a new answer to data processing in monitoring systems by using advancements in IoT, artificial intelligence, and transmission technologies to analyze data regionally at the edge. This research explores the viability of using edge computing for smart parking monitoring, focusing on detecting parking occupancy via a real-time video stream. The structure operating pipeline to prioritize adaptability, online surveillance, data transfer, detection precision, and system dependability. The experimental findings show that the final detection approach achieves more than 95% accuracy in real-world situations with outstanding effectiveness and reliability. The IoT-PSS system is essential for smart cities and is a strong basis for future use in smart transportation networks.

Vidya H Veldore,1,* Anuradha Choughule,2,* Tejaswi Routhu,1 Nitin Mandloi,1 Vanita Noronha,2 Amit Joshi,2 Amit Dutt,3 Ravi Gupta,1 Ramprasad Vedam,1 Kumar Prabhash2 1MedGenome Labs Private Ltd,, Bangalore, India; 2Tata Memorial Centre, Parel, Mumbai, India; 3The Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Center, Kharghar, Navi Mumbai, Maharashtra, India *These authors contributed equally to this work Abstract: Plasma cell-free tumor DNA, or circulating tumor DNA (ctDNA), from liquid biopsy is a potential source of tumor genetic material, in the absence of tissue biopsy, for EGFR testing. Our validation study reiterates the clinical utility of ctDNA next generation sequencing (NGS) for EGFR mutation testing in non-small cell lung cancer (NSCLC). A total of 163 NSCLC cases were included in the validation, of which 132 patients had paired tissue biopsy and ctDNA. We chose to validate ctDNA using deep sequencing with custom designed bioinformatics methods that could detect somatic mutations at allele frequencies as low as 0.01%. Benchmarking allele specific real time PCR as one of the standard methods for tissue-based EGFR mutation testing, the ctDNA NGS test was validated on all the plasma derived cell-free DNA samples. We observed a high concordance (96.96%) between tissue biopsy and ctDNA for oncogenic driver mutations in Exon 19 and Exon 21 of the EGFR gene. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of the assay were 91.1%, 100% 100%, 95.6%, and 97%, respectively. A false negative rate of 3% was observed. A subset of mutations was also verified on droplet digital PCR. Sixteen percent EGFR mutation positivity was observed in patients where only liquid biopsy was available, thus creating options for targeted therapy. This is the first and largest study from India, demonstrating successful validation of circulating cell-free DNA as a clinically useful material for molecular testing in NSCLC. Keywords: liquid biopsy, NSCLC, EGFR, ctDNA, NGS

Purpose: Connected speech analysis has been effectively utilized for the diagnosis and disease monitoring of individuals with Alzheimer's disease (AD). Existing research has been conducted mostly in monolingual English speakers with a noticeable lack of evidence from bilinguals and non-English speakers, particularly in non-European languages. Using a case study approach, we characterized connected speech profiles of two Bengali--English bilingual speakers with AD to determine the universal features of language impairments in both languages, identify language-specific differences between the languages, and explore language impairment characteristics of the participants with AD in relation to their bilingual language experience. Method: Participants included two Bengali--English bilingual speakers with AD and a group of age-, gender-, education-, and language-matched neurologically healthy controls. Connected speech samples were collected in first language (L1; Bengali) and second language (L2; English) using a novel storytelling task (i.e., Frog, Where Are You?). These samples were analyzed using an augmented quantitative production analysis and correct information unit analyses for productivity, fluency, syntactic and morphosyntactic features, and lexical and semantic characteristics. Results: Irrespective of the language, AD impacted speech productivity (speech rate and fluency) and semantic characteristics in both languages. Unique language-specific differences were noted on syntactic measures (reduced sentence length in Bengali), lexical distribution (fewer pronouns and absence of reduplication in Bengali), and inflectional properties (no difficulties with noun or verb inflections in Bengali). Among the two participants with AD, the individual who showed lower proficiency and usage in L2 (English) demonstrated reduced syntactic complexity and morphosyntactic richness in English. Conclusions: Evidence from these case studies suggests that language impairment features in AD are not universal across languages, particularly in comparison to impairments typically associated with language breakdowns in English. This study underscores the importance of establishing connected speech profiles in AD for non--English-speaking populations, especially for structurally different languages. This would in turn lead to the development of language-specific markers that can facilitate early detection of language deterioration and aid in improving diagnosis of AD in individuals belonging to underserved linguistically diverse populations.