Limited Scalability Research Articles

Automatic telephonic follow up in heart failure by a natural language processing artificial intelligence: pilot program

Abstract Introduction Heart failure (HF) burden on public health is substantial and efficient patient management strategies are needed. Traditional methods of HF monitoring face limitations in scalability and effectiveness. Artificial Intelligence (AI) might offer novel solutions for enhanced resource management and alleviation of healthcare burdens. Purpose To study the feasibility of implementing automatic remote monitoring via phone calls operated by AI in a HF unit. Methods We conducted a non-randomized, open, pilot trial employing an AI technology based on natural language processing for autonomous telephone outreach for the follow up of HF patients. 86 patients from a tertiary hospital HF unit undergoing pharmacological titration and education were enrolled. The AI conducted weekly calls to gather symptomatology, vital signs, and health status changes, which were then displayed on a virtual platform featuring alerts preconfigured by the research team. These alerts guided patient communication and intervention as deemed necessary by a specialized nurse. Results Characteristics of patients are shown in Table 1. The AI followed these patients for a median of 251.5 days. 2773 telephonic calls were performed. We estimate an equivalent to 1445h of work (17.2 days/month). Adherence was high during the study, with 98% of the calls completed. 1427 alerts were generated, being the most frequent weight change (251; 17.6%), orthopnea (220; 15.4%) and fatigue (187; 13.1%). It led to 487 remote interventions, most of them consisting in calling the patient for assessment (409; 84.0%), but also in adjusting medication remotely (34; 7.0%). See Figure 2. A HF telemedicine specialized nurse required 205min per week for alert review, 2.38min/patient-week. 2 patients died, one from cardiovascular causes, and 7 patients had a HF hospitalization, 42.9% with previous alarms. 20 events of worsening HF were recorded, comprising hospitalization, emergency visit or IV diuretic administration. Oral diuretic dose was increased in 22 occasions, 15 of them remotely in response to an alert raised by the AI operator. At 6 months functional class globally improved (p = 0.028), but NTproBNP increased (p=0.0472). Quality of life was evaluated at baseline and at 6 months, with no change in EQ5D5L (median 0.717, change -0.005, p 0.71) or PHQ4 (median 2.88, change -0.29, p 0.7792), but KCCQ12 total score worsened (median 74.49, change -2.32, p 0.048). Experience was rated with 8.97/10 in a satisfaction questionnaire. All patients recommended the program, remarking a closer follow up, better communication and a safety feeling. Conclusions AI enables closer monitoring of patients with HF, maintaining contact with stable patients while also allowing for resource allocation and tracking of higher-risk patients or those experiencing changes in symptoms. A randomized clinical trial comparing to standard follow-up is needed to evaluate this promising technology.Table 1Figure 2

Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems

This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.

Advances in Plant Disease Diagnostics and Surveillance- A review

Plant diseases pose a significant threat to global food security, leading to substantial yield losses and economic impacts. Early detection and effective monitoring are crucial for managing plant diseases, yet traditional diagnostic methods such as visual inspections, serological tests, and molecular assays face limitations in sensitivity, specificity, and scalability. In recent years, advancements in diagnostic and surveillance technologies have revolutionized plant health management. Next-Generation Sequencing (NGS) enables comprehensive pathogen profiling, while CRISPR-based diagnostics offer rapid and highly specific detection. Similarly, biosensors and portable devices provide on-site diagnostics, and machine learning and AI applications enhance the analysis of complex datasets, supporting automated disease identification and predictive modeling. Concurrently, advances in disease surveillance through remote sensing technologies, including satellites and Unmanned Aerial Vehicles (UAVs), enable large-scale, real-time monitoring of crop health, detecting disease outbreaks and facilitating targeted interventions. Integrating these diverse technologies into multi-platform systems offers a holistic approach to plant disease management, combining molecular diagnostics, environmental monitoring, and digital platforms to support data-driven decision-making. Several challenges remain, including high costs, technical complexities, and the need for standardized data integration. Addressing these barriers is essential to ensure that these technologies are accessible and effective across various agricultural systems, particularly in resource-limited settings. Future research should focus on enhancing the robustness, affordability, and scalability of these tools while promoting interdisciplinary collaborations.

Carbon Carriers Driving the Net-Zero Future: The Role of Torrefied Biomass Pellets in Power-To-X

The latest Intergovernmental Panel on Climate Change Sixth Assessment Report urgently calls for sweeping action to mitigate the unprecedented impacts of climate change. The path to a carbon-neutral future is intricate, necessitating a multi-faceted approach that integrates decarbonization, defossilization, and energy/resource efficiency. Power-to-X (PtX) stands as a technological linchpin, converting renewable electricity into a range of sustainable products, from fuels to chemicals. However, its full potential is intrinsically tied to the availability of sustainable carbon sources. This paper evaluates the various avenues for carbon sourcing for PtX: direct air capture (DAC), biogenic carbon, and Long-cycle Industrial Carbon. DAC, although promising for the long term, has limitations in scalability and land requirements. Industrial long-cycle carbon capture technology is improving but requires a thorough Life Cycle Assessment for evaluating its sustainability. This study examines the environmental impacts, scalability, and logistical considerations of each carbon source. Biogenic carbon offers a near-term solution, and its various forms could simplify transportation logistics. An analysis of gasification processes, syngas cleaning, and hydrogen integration was conducted to assess the technical viability of these carbon sources in PtX applications. The results show that torrefied biomass pellets, after a thorough technical assessment, present a globally feasible and sustainable carbon carrier, setting the stage for industry standardization and easier global transportation. Syngas produced through the gasification of the pellets complemented by green hydrogen can be utilized in Fischer–Tropsch, methanol synthesis, and methanation, allowing PtX to synthesize practically any type of organic compounds in a hybrid Biomass–PtX (HBPtX) process. This study provides key insights for industries and policymakers by demonstrating the technical feasibility and sustainability of torrefied biomass as a carbon carrier, thereby supporting the development of comprehensive climate mitigation strategies.

Mitigating Grand Challenges in Life Cycle Inventory Modeling through the Applications of Large Language Models.

The accuracy of life cycle assessment (LCA) studies is often questioned due to the two grand challenges of life cycle inventory (LCI) modeling: (1) missing foreground flow data and (2) inconsistency in background data matching. Traditional mechanistic methods (e.g., process simulation) and existing machine learning (ML) methods (e.g., similarity-based selection methods) are inadequate due to their limitations in scalability and generalizability. The large language models (LLMs) are well-positioned to address these challenges, given the massive and diverse knowledge learned through the pretraining step. Incorporating LLMs into LCI modeling can lead to the automation of inventory data curation from diverse data sources and to the implementation of a multimodal analytical capacity. In this article, we delineated the mechanisms and advantages of LLMs to addressing these two grand challenges. We also discussed the future research to enhance the use of LLMs for LCI modeling, which includes the key areas such as improving retrieval augmented generation (RAG), integration with knowledge graphs, developing prompt engineering strategies, and fine-tuning pretrained LLMs for LCI-specific tasks. The findings from our study serve as a foundation for future research on scalable and automated LCI modeling methods that can provide more appropriate data for LCA calculations.

Single-Use Technology in Purification of Monoclonal Antibiotics

The advent of Single-Use Technologies (SUT) has revolutionized biopharmaceutical manufacturing, offering a flexible and efficient alternative to traditional methods. This paper explores the diverse applications of SUT, focusing on therapeutic plasma filtration, clarifying processes, virus removal strategies, and chromatography solutions. Highlighting the advantages such as convenience, reduced contamination risk, and streamlined processes, the document delves into the economic and environmental impacts of adopting SUT. Despite challenges in scalability and automation, specific examples like Immusorba PH-350 and Pall Biotech's AllegroTM underscore the significant adsorption efficiency achieved with single-use systems. Anticoagulation strategies tailored for single-use plasma filtration contribute to optimal systemic performance. The dynamic market for disposable devices, featuring prominent manufacturers like Fresenius HemoCare, Kaneka, Medicap Clinic GmbH, Miltenyi Biotec, Toray Medical, and Biotex, reflects the widespread adoption of SUT across various medical applications. The ongoing innovation in overcoming scalability limitations and the commitment to standardization position SUT as a transformative force in modern biopharmaceutical manufacturing. In conclusion, the economic implications and positive environmental impact underscore SUT's potential to shape the future of the industry. Keywords: Single Use Technology, Reduced Contamination, Antibiotics, Biopharmaceutical, Good Manufacturing Practices, Mixing bag, Aseptic Connections, Single use Bioreactors, Disposable Chromatography Columns, Single use Filtration, Running Cost, Labour charges

Ultrafast Synthesis of MXenes in Minutes via Low-Temperature Molten Salt Etching.

Developing green and efficient preparation strategies is a persistent pursuit in the field of 2D transition metal nitrides and/or carbides (MXenes). Traditional etching methods, such as HF-based or high-temperature Lewis-acid-molten-salt etching route, require harsher etching conditions and exhibit lower preparation efficiency with limited scalability, severely constraining their commercial production and practical application. Here, an ultrafast low-temperature molten salt (LTMS) etching method is presented for the large-scale synthesis of diverse MXenes within minutes by employing NH4HF2 as the etchant. The increased thermal motion and improved diffusion of molten NH4HF2 molecules significantly expedite the etching process of MAX phases, thus achieving the preparation of Ti3C2Tx MXene in just 5minutes. The universality of the LTMS method renders it a valuable approach for the rapid synthesis of various MXenes, including V4C3Tx, Nb4C3Tx, Mo2TiC2Tx, and Mo2CTx. The LTMS method is easy to scale up and can yield more than 100g Ti3C2Tx in a single reaction. The obtained LTMS-MXene exhibits excellent electrochemical performance for supercapacitors, evidently proving the effectiveness of the LTMS method. This work provides an ultrafast, universal, and scalable LTMS etching method for the large-scale commercial production of MXenes.

Enhancing Healthcare Information Systems in Ethiopian Hospitals: Exploring Challenges and Prospects of a Cloud-based Model for Smart and Sustainable Information Services

Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions. Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.

Enhancing vibroacoustic reduced order models for digital twin development in industrial applications

In recent years, the growing interest towards the Digital Twin has prompted extensive exploration in both academic and industrial domains. To realize this technology, the development of simulation models that seamlessly run parallel to the physical assets is necessary. Model Order Reduction (MOR) plays a pivotal role, significantly easing the computational demands of 3D models while maintaining high fidelity. Krylov-based methods have proven successful in various vibroacoustic applications. However, state-of-the-art contributions employ suboptimal modeling strategies, leading to computationally inefficient offline phases and limited scalability. The linear Finite Element Method (FEM) exhibits lower efficiency than high-order FEM; Astley-Leis infinite elements, used to enforce non-reflecting conditions, constrain models to spherical domains, proving inefficient for elongated structures. In this work, advanced techniques-FEM Adaptive Order (FEMAO) and flexible infinite elements- are employed to alleviate the computational burden of the MOR process. The advantages of combining MOR, FEMAO, and flexible infinite elements are explored through various numerical examples. Specifically, we highlight the efficiency of output-based MOR in handling models with a high number of inputs, showcasing practical applications, especially in electric motor noise analysis. In conclusion, these numerical techniques reduce the computational burden of the offline phase, unlocking the Digital Twin for large-scale industrial models.

Piezoelectric Energy Harvesting for Civil Engineering Applications

This work embarks on an exploration of piezoelectric energy harvesting (PEH), seeking to unravel its potential and practicality. PEH has emerged as a promising technology in the field of civil engineering, offering a sustainable approach to generating energy from ambient mechanical vibrations. We will explore the applications and advancements of PEH within the realm of civil engineering, focusing on publications, especially from the years 2020 to 2024. The purpose of this study is to thoroughly examine the potential and practicality of PEH in civil engineering applications. It delves into the fundamental principles of energy conversion and explores its use in various areas, such as roadways, railways, bridges, buildings, ocean wave-based energy harvesting, structural health monitoring, and even extraterrestrial settings. Despite the potential benefits of PEH in these domains, there are significant challenges that need to be addressed. These challenges include inefficient energy conversion, limitations in scalability, concerns regarding durability, and issues with integration. This review article aims to address these existing challenges and the research gap in the piezoelectric field.

A SYSTEMATIC REVIEW OF BIG DATA INTEGRATION CHALLENGES AND SOLUTIONS FOR HETEROGENEOUS DATA SOURCES

This systematic review explores the current challenges and emerging solutions in big data integration, focusing on key issues such as semantic heterogeneity, data quality, scalability, and security. Using the PRISMA guidelines, 150 peer-reviewed articles were analyzed to identify both established and innovative approaches to integrating data from heterogeneous sources. The findings reveal that ontology-based frameworks are widely used to address semantic inconsistencies but face limitations in scalability when handling large, dynamic datasets. Machine learning has emerged as a powerful tool for automating data quality and schema matching processes, although its effectiveness is highly dependent on the availability of high-quality training data. Distributed computing frameworks like Hadoop and Spark have become the industry standard for scalable data integration, yet their implementation requires significant infrastructure and technical expertise. Cloud-based platforms offer flexible, scalable solutions, but concerns about data privacy and security persist. Blockchain technology, while promising for secure and decentralized data integration, is still in its infancy and struggles with scalability. The review highlights significant progress in the field but underscores the need for further research to address unresolved challenges in real-time integration, cross-domain data harmonization, and the management of unstructured data.

Enabling efficient and low-effort decentralized federated learning with the EdgeFL framework

Context:Federated Learning (FL) has gained prominence as a solution for preserving data privacy in machine learning applications. However, existing FL frameworks pose challenges for software engineers due to implementation complexity, limited customization options, and scalability issues. These limitations prevent the practical deployment of FL, especially in dynamic and resource-constrained edge environments, preventing its widespread adoption. Objective:To address these challenges, we propose EdgeFL, an efficient and low-effort FL framework designed to overcome centralized aggregation, implementation complexity and scalability limitations. EdgeFL applies a decentralized architecture that eliminates reliance on a central server by enabling direct model training and aggregation among edge nodes, which enhances fault tolerance and adaptability to diverse edge environments. Methods:We conducted experiments and a case study to demonstrate the effectiveness of EdgeFL. Our approach focuses on reducing weight update latency and facilitating faster model evolution on edge devices. Results:Our findings indicate that EdgeFL outperforms existing FL frameworks in terms of learning efficiency and performance. By enabling quicker model evolution on edge devices, EdgeFL enhances overall efficiency and responsiveness to changing data patterns. Conclusion:EdgeFL offers a solution for software engineers and companies seeking the benefits of FL, while effectively overcoming the challenges and privacy concerns associated with traditional FL frameworks. Its decentralized approach, simplified implementation, combined with enhanced customization and fault tolerance, make it suitable for diverse applications and industries.

Non-Volatile Resistive Switching in Nanoscaled Elemental Tellurium by Vapor Transport Deposition on Gold.

Two-dimensional (2D) materials are promising for resistive switching in neuromorphic and in-memory computing, as their atomic thickness substantially improve the energetic budget of the device and circuits. However, many 2D resistive switching materials struggle with complex growth methods or limited scalability. 2D tellurium exhibits striking characteristics such as simplicity in chemistry, structure, and synthesis making it suitable for various applications. This study reports the first memristor design based on nanoscaled tellurium synthesized by vapor transport deposition (VTD) at a temperature as low as 100°C fully compatible with back-end-of-line processing. The resistive switching behavior of tellurium nanosheets is studied by conductive atomic force microscopy, providing valuable insights into its memristive functionality, supported by microscale device measurements. Selecting gold as the substrate material enhances the memristive behavior of nanoscaled telluriumin terms of reduced values of set voltage and energy consumption. In addition, formation of conductive paths leading to resistive switching behavior on the gold substrate is driven by gold-tellurium interface reconfiguration during the VTD process as revealed by energy electron loss spectroscopy analysis. These findings reveal the potential of nanoscaled tellurium as a versatile and scalable material for neuromorphic computing and underscore the influential role of gold electrodes in enhancing its memristive performance.

From pollutants to products: Microbial cell factories driving sustainable biomanufacturing and environmental conservation

Microbial cell factories are emerging as powerful tools in addressing pressing environmental challenges and promoting sustainable biomanufacturing. This paper highlights the key role of engineered microorganisms in mitigating pollution, converting waste and pollutants into valuable products, and reducing reliance on fossil fuels. Through advancements in metabolic engineering, gene editing technologies, and synthetic biology, microbial cell factories are being optimized to enhance their efficiency in breaking down pollutants and producing renewable chemicals, such as biofuels, bioplastics, and specialty chemicals. These processes contribute to environmental conservation, waste valorization, and the establishment of a circular economy.The study focuses on overcoming key barriers in microbial biotechnology, such as limited scalability, process inefficiency, and economic viability, by employing strategies like metabolic pathway optimization, enzyme overexpression, and tolerance enhancement. These strategies are applied to various microbial species, demonstrating how their metabolic capabilities can be fine-tuned for industrial applications. Detailed case studies illustrate successful implementations, such as the conversion of lignocellulosic biomass, CO2, and industrial waste into high-value products, underscoring the practical impact of microbial cell factories in diverse sectors, including energy, materials, and chemicals.Furthermore, this research addresses the challenges faced by microbial cell factories in industrial-scale operations, such as maintaining genetic stability and optimizing growth conditions, and offers insight into emerging technological solutions to these obstacles. By providing a comprehensive overview of recent developments and identifying future research directions, this paper offers actionable recommendations for unlocking the full potential of microbial biotechnology. These efforts aim to further integrate microbial processes into industrial systems, contributing to a more sustainable and resilient global economy, with the ultimate goal of fostering a circular bioeconomy.

Categorizing E-cigarette-related tweets using BERT topic modeling

BackgroundSocial media platforms are critical channels for promoting e-cigarettes, particularly among youth, making analysis of their vast and diverse content essential for public health interventions. Prevalence rates of e-cigarette use are high and evidence suggests that social media are popular forums that promote e-cigarette use through direct and indirect marketing techniques. The volume and diverse nature of e-cigarette-related information on social media is challenging and may obfuscate public health prevention messaging. Traditional hand-coding methods are labor-intensive and limit scalability. In contrast, unsupervised machine learning approaches, such as topic modeling, allow for efficient analysis of large datasets, uncovering patterns and trends that manual methods cannot achieve at scale. The present study focused on ascertaining the extent to which themes and topics in tweets related to e-cigarettes can be successfully rendered into useful homogenous units using machine learning. A better understanding of current depictions and discussions around e-cigarette products and use on social media can inform public health counter messaging and policy interventions. MethodsWe used topic modeling (BERTopic) to iteratively derive vape-related tweet clusters and calculate the importance of particular words to these groupings. We conducted a qualitative content analysis to study clustered tweets. We also sought to determine the geographic locations of e-cigarette conversations using automated geoparsing methods, which translate toponyms in textual data into geographic identifiers, to attempt to infer the location of tweets. ResultsWe were able to successfully identify >100,000 tweets in broad thematic categories in English and Spanish. Our correlation and inter-topic map analysis of the machine-derived topics, which examines the relationships between topics, indicated that most of the topics were unique (correlation value < 0.5) and did not overlap with each other. We identified six topics: Flavors and Disposable Vapes, Cannabis, Vape Shops and Refillable Vapes, Vape Culture, Anti-vaping and Quitting, and Spanish Tweets and Vaping Nicotine. Further analysis of these topics using qualitative methods identified themes within each topic. For example, Category 6 (Spanish Tweets and Vaping Nicotine) included four topics focused on the health risks of vaping, personal motivations for vaping, and the regulation of vaping products. Using geoparsing, which automatically detects location information, we found that the United States had the highest number of tweets related to vaping. Discussion/conclusionResults underscore the possibility of leveraging BERTopic modeling to reduce large quantities of data to comprehensively describe and categorize myriad e-cigarette related messages to which social media users are exposed. This data reduction approach can be applied to various social media platforms to describe and categorize e-cigarette posts and thereby triangulate and validate findings. Thematic content analysis of the topics identified through this technique requires supervision and human inputs. Our approach provides a comprehensive understanding of the evolving e-cigarette discourse, informing public health counter-messaging and policy interventions. Moreover, findings support the need for regulation, such as reducing appealing flavors and suggest that social media can be used effectively to support public health messaging (i.e., quitting messages).

Highly Photoresponsive Vertically Stacked Silicon Nanowire Photodetector with Biphasic Current Stimulator IC for Retinal Prostheses

This paper presents an integrated approach for a retinal prosthesis that overcomes the scalability challenges and limitations of conventional systems that use external cameras. Silicon nanowires (SiNWs) are utilized as photonic sensors due to their nanoscale dimensions and high surface-to-volume ratio. To enhance these properties and achieve high photoresponsivity, our research team developed a vertically stacked SiNW structure using a fabrication method entirely based on dry etching. The fabricated SiNW photodetector demonstrated excellent electrical and optical characteristics, including linear I–V characteristics that confirmed ohmic contact formation and high photoresponsivity exceeding 105 A/W across the 400–800 nm wavelength range. The SiNW photodetector, following its integration with a switched capacitor stimulator circuit, exhibited a proportional increase in stimulation current in response to higher light intensity and increased SiNW density. In vitro experiments confirmed the efficacy of the integrated system in inducing neural responses from retinal cells, as indicated by an increased number of neural spikes observed at higher light intensities and SiNW densities. This study contributes to sensor technology by demonstrating an approach to integrating nanostructures and electronic components, which enhances control and functionality.

Comparative Analysis of Hybrid Geothermal-Solar Systems and Solar PV with Battery Storage: Site Suitability, Emissions, and Economic Performance

Renewable energy integration has become a critical focus in the global effort to reduce carbon emissions and diversify energy sources. In regions with distinct geographic features, such as Türkiye, combining different renewable technologies can offer enhanced energy security. This study investigates the site suitability and economic and environmental performance of hybrid geothermal-solar systems and solar PV systems with battery storage across the provinces of Osmaniye, Hatay, and Kilis, of Türkiye. Using the fuzzy-AHP method, site suitability is evaluated, addressing a key gap in comparing these systems' adaptability to varying geographic conditions. This study is the first to directly compare these two renewable energy technologies in terms of site suitability. The findings reveal significant differences in site suitability, with solar PV systems with battery storage demonstrating broader applicability across the region. The suitable sites (20-100% suitability) cover 1260.82 km² for solar PV systems with battery storage and only 122.18 km² for hybrid geothermal-solar systems. In terms of environmental impact, hybrid geothermal-solar systems exhibit significantly lower carbon emissions, averaging 44.6 kg CO₂/MWh, compared to 123.8 kg CO₂/MWh for solar PV systems with battery storage. Economically, hybrid geothermal-solar systems also outperform with a lower levelized cost of electricity of $0.091/kWh versus $0.254/kWh for solar PV systems. These results highlight the environmental and economic advantages of hybrid geothermal-solar systems, while also emphasizing their limited scalability to regions with geothermal activity. Conversely, solar PV systems, despite their higher emissions and costs, offer greater flexibility and potential for widespread deployment.

A Link’s Fineness Based on Spatial Relation for Multipoint Relays Selection Algorithm in Mobile Ad Hoc Networks Algorithm

The propagation of wireless mobile equipment has given rise to mobile ad hoc networks (MANET), which allow the establishment of networks deprived of infrastructure support. The improvements in these technologies have also resulted in the variety of link quality, scalability, stability and reachability limits. Principally, due to their incapability to guarantee node reliability, MANETs are weak in highly dynamic environment. Since high-speed nodes, low density, and rapid changes in the topology are directly damage networks performances and lifetime. In response, routing is an inspiring issue in these networks. This paper proposed a new routing scheme in MANETS. This technique is integrated in the optimized link state routing (OLSR). In the projected scheme, the author defines a link fineness based on spatial relation between mobile nodes, which exploits different parameters, speed, direction and acceleration, including the sent and received of hello packets. Correspondingly, the proposed selected multipoint relays (MPRs) based the projected methods is included in the algorithm. The scheme elected more stable MPRs with high link’s quality, more neighborhood degree and higher willingness as MPR that can increase the reachability and stability of the network even in an extremely dynamic situation. This version of OLSR is verified by the network simulator (NS3) under the Radom Waypoint mobility model. Results revealed an important improvement for SLF_OLSR. In addition, this scheme can be integrated into other protocols in MANETs.

Designing A Blockchain-Empowered Telehealth Artifact for Decentralized Identity Management and Trustworthy Communication: Interdisciplinary Approach.

Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks. The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance. We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype implementation. We used the design science research methodology to implement our overall design. We innovated over existing telehealth platforms with blockchain integration that improves health care delivery services in terms of security, privacy, and efficiency. We adopted a user-centric design approach and started with user requirement collection, followed by system functionality development. Overall system implementation facilitates user requirements, thus promoting user behavior for the adoption of the telehealth platform with decentralized identity management and an access control mechanism. Our investigation identified key challenges to identity management and trustworthy communication processes in telehealth platforms used in the current health care domain. By adopting distributed ledger technology, we proposed a decentralized telehealth platform to support identity management and a trustworthy communication process. Our design and prototype implementation using a smart contract-driven telehealth platform to provide decentralized identity management and trustworthy communication with token-based access control addressed several security challenges. This was substantiated by testing with 10,000 simulated transactions across 5 peers in the Rahasak blockchain network. The proposed design provides resistance to common attacks while maintaining a linear time overhead, demonstrating improved security and efficiency in telehealth services. We evaluated the performance in terms of transaction throughput, smart contract execution time, and block generation time. To create a block with 10,000 transactions, it takes 8 seconds on average, which is an acceptable overhead for blockchain-based applications. We identified technical limitations in current telehealth platforms. We presented several design innovations using blockchain to prototype a system. We also presented the implementation details of a unique distributed architecture for a trustworthy communication system. We illustrated how this design can overcome privacy, security, and scalability limitations. Moreover, we illustrated how improving these factors sets the stage for improving and standardizing the application and for the wide adoption of blockchain-enabled telehealth platforms.

Exploring Biosurfactants: Sustainable Solutions for Overcoming Antimicrobial Resistance and Drug Adverse Effects

Antimicrobial resistance (AMR) poses a significant global health threat, necessitating the exploration of alternative strategies to combat microbial pathogens. Biosurfactants, natural compounds produced by microorganisms, have emerged as promising candidates due to their diverse biological activities and sustainable properties. This review aims to provide a comprehensive overview of biosurfactants' potential as sustainable solutions for overcoming AMR and mitigating drug adverse effects. We discuss the mechanisms of action of biosurfactants against microbial pathogens, highlighting their ability to disrupt cell membranes, inhibit biofilm formation, and modulate immune responses. Furthermore, we explore the biocompatibility and eco-friendly nature of biosurfactants, underscoring their advantages over synthetic drugs. However, challenges such as limited scalability and variability in production hinder their widespread adoption in pharmaceutical applications. We propose interdisciplinary research efforts to address these challenges and optimize the use of biosurfactants in combating AMR and minimizing drug adverse effects. Through collaborative initiatives and innovative approaches, biosurfactants hold immense promise as sustainable alternatives to conventional antimicrobial agents, paving the way for a healthier and more resilient future.