Related Schemes Research Articles

Facilitating generational renewal in rural areas by responding to young farmers’ voices: Echoes from the Greek territory

The proper functioning and future of rural areas and communities are strictly tied to young people’s willingness to be engaged in the farming profession, as farming is an important recourse for vocational rehabilitation of the rural population. The agricultural sector acts as a financial injection to the rural economy and society. In this context, one of the most well devised agricultural policies is young farmers’ schemes, aiming at generational renewal in EU rural areas. Since young farmers are the people who directly receive the induced effects of these policies, policy makers have to listen carefully to what “message” young farmers can convey. Nevertheless, after almost four decades of implementing young farmers’ schemes, there is still limited information regarding their attitudes, beliefs and perceptions towards the form of the related policy schemes. Using this as a starting point, the present study attempts to identify young farmers’ attitudes and beliefs towards the current young farmers’ scheme of the Rural Development Program (RDP) with overarching scope to identify the needs of young farmers better and thus inform policy makers about the appropriate policy that should be put forward. Results indicate that the current young farmers’ scheme acts as a helpful instrument for a considerable percentage of young farmers. Nevertheless, further streamlining is required to provide incentives, especially to new entrants, to be engaged in the farming profession. Effective policy interventions that will be in line with the actual needs of young farmers could contribute to the direction of the enhancement of the vitality and resilience of the rural systems, and also act towards the prevention of the abandonment process, providing vibrance in rural areas and benefits for the whole economy and society, as well as the ecosystems.

Operation status monitoring for 500 kV DC circuit breaker with internal failures and relative backup fault isolation schemes

As the primary protection and control device in direct current (DC) power systems, DC circuit breakers (DCBs) have complex structures and numerous components, making it prone to diversified internal failures. This paper explores an operation status monitoring method for DCBs, and relative backup fault isolation schemes and system recovery schemes are proposed to handle cases of DCBs with different internal failures. Contributions of this paper are: (1) An operation status monitoring method is proposed to reflect the failure degrees of DCB, categorizing the statuses of DCB into status-1 (normal), status-2 (minor failure) and status-3 (major failure). (2) According to the failure degrees of DCB, relative backup fault isolation and system recovery schemes are proposed to ensure the fault isolation scope is as small as possible and the system could recovery as fast as possible. Simulations are conducted in PSCAD/EMTDC for validation.

Modeling Nonspherical Hailstones

Abstract Hail research and forecasting models necessarily involve explicit or implicit—and uncertain—physical assumptions regarding hailstones’ shape, tumbling behavior, fall speed, and thermal energy transfer. Whereas most models assume spherical hailstones, we relax this assumption by using hailstone shape data from field observations to establish empirical size–shape relationships with reasonable degrees of randomness considering hailstones’ natural shape variability, capturing the observed distribution of triaxial ellipsoidal shapes. We also incorporate explicit, random tumbling of individual hailstones during their growth to simulate their free-falling behavior and the resultant changes in cross-sectional area (which affects growth by hydrometeor collection). These physical attributes are incorporated in calculating hailstones’ fall speeds, using either empirical relationships or analytical relationships based on each hailstone’s Best and Reynolds numbers. Options for drag coefficient modification are added to emulate hailstones’ rough surfaces (lobes), which then modifies their thermal energy and vapor exchange with the environment. We investigate how applying these physical assumptions about nonspherical hail to the Penn State hail growth trajectory model, coupled with Cloud Model 1 supercell simulations, impacts hail production and examine the reasons behind the resulting variability in hail statistics. The choice of hailstone size–mass relation and fall speed scheme have the strongest influence on hail sizes. Using nonspherical, tumbling hailstones reduces the number of large hailstones produced. Applying shape-specific thermal energy transfer coefficients subtly increases sizes; the effects of lobes vary depending on the fall speed scheme used. These physical assumptions, although adding complexity to modeling, can be parameterized efficiently and potentially used in microphysics schemes. Significance Statement In numerical modeling of hailstorms, we usually consider hailstones to be spherical to simplify calculations, but in nature, hailstones generally are not spheres and can be rather lumpy and have spikes. The purpose of this study is to examine how the model result would change when nonspherical hailstone shape is implemented. We examine the relationship between hailstone shape and physical processes during hail growth in effort to explain why these changes occur and offer insights on how nonspherical hailstone shape may be parameterized in bulk microphysics schemes.

Event-Triggered Adaptive Finite-Time Control Using a Fuzzy State Observer for Nonlinear Cyber-Physical Systems Under Deception Attacks.

This article presents a new event-triggered adaptive finite-time control strategy using a fuzzy state observer for a class of nonlinear cyber-physical systems (CPSs) under malicious deception attacks with a more general form. Compared with the traditional assumptions on the deception attacks in the existing results, a more general assumption on deception attacks is given in this article. During the design process, real system states are initially estimated by developing an improved state observer, which effectively addresses the problem of state unavailability. Then, a coordinate transformation technology, in which the estimated states of observer are considered, is presented to stabilize the studied system. By constructing the singularity-free finite time virtual controls, the singularity problem in the traditional finite time design algorithms is cleverly avoided. Furthermore, to minimize communication overhead, a final finite-time controller is established by using a relative threshold event-triggered scheme. The developed event-triggered adaptive finite-time control strategy guarantees that all signals in the closed-loop system are semi-globally bounded in finite time without Zeno behavior. Finally, the correctness of the proposed control strategy is validated through two simulation results.

Study on the differences in microscopic oil displacement effects and action mechanisms of different rhamnolipid systems

Rhamnolipids are a class of anionic glycolipid surfactants produced through microbial metabolism. As a widely researched biosurfactant, rhamnolipids possess several advantages over traditional chemical surfactants, including non-toxicity, eco-friendliness, biodegradability, and biocompatibility, particularly in the context of microbial oil recovery applications. This class of surfactants enhances oil recovery by reducing the interfacial tension between oil and water, emulsifying residual oil, and modifying the wettability of rock surfaces. Furthermore, rhamnolipids maintain stability in high-temperature and high-salinity environments. However, rhamnolipids derived from different fermentation substrates exhibit variations in structure, composition, and properties, resulting in distinct displacement effects and mechanisms of action. This study focuses on two types of rhamnolipids: typical rhamnolipid and high-yield rhamnolipid, which are fermented using glycerol and rapeseed oil, respectively. Based on the characteristics of the target heavy oil reservoir, micromodels were designed and manufactured to conduct microfluidic experiments. The results obtained from imaging and video recording were analyzed qualitatively and quantitatively to explore the differences in effects and mechanisms between the two rhamnolipid systems. Results indicate that typical rhamnolipid increased recovery by 4.41% through delayed mechanisms involving wettability modification and residual oil emulsification. Conversely, high-yield rhamnolipid demonstrates an immediate effect by reducing interfacial tension, resulting in a recovery increase in 5.68%. According to the observed experimental phenomena and analytical trends, the conclusions evaluate the production increase, clarify the differences in mechanisms of action, and enhance the microscopic understanding of these surfactants. These findings provide directions for future investigations and serve as a reference for the design of related schemes.

Development of a new digital sexual health assessment tool to help patients better understand the factors involved in their sexual disorders – “The Sexual Dysfunction Evaluation Tool” (SEXDET)

IntroductionA visual digital tool called SEXual Dysfunction Evaluation Tool (SEX-DET) was created for assessing the causes of sexual dysfunction, based on a biopsychosocialsexual and relational schema, to assist patients in understanding the relative influence of the various factors contributing to their sexual dysfunction. The aim of this study was to evaluate satisfaction of this tool in sexual dysfunction patients and practitioners. MethodsA satisfaction study of the SEX-DET was performed in 50 patients (20 F, 30 M) consulting for a sexual trouble and 3 practitioners. The main reasons for consultation were erectile dysfunction (36%) and desire disorder (30%). ResultsAll the participants agreed that the SEX-DET helped them to understand the origin their trouble (46% strongly, 42% moderately, 12% somewhat), the various factors involved (56% strongly, 22% moderately and 18% somewhat) and the management of their trouble (6% strongly, 47% moderately, 33% somewhat). Overall, 94% of participants found that the SEX-DET was easy to use (58% strongly, 16% moderately and 20% somewhat). Practitioners acknowledged the user-friendly interface, its critical role in improving patients’ understanding and providing a comprehensive visualization of the dimensions associated with sexual dysfunction, even if one practitioner threatened the tool's intrinsic inclination to oversimplify complex data. ConclusionSEX-DET enabled to improve patients’ understanding of their trouble and helped practitioners in structuring better communication during consultations communication regarding sexual dysfunction. The shortcomings of this study was its focus on short-term outcomes, self-reported data, and the absence of control group.

Synthesis of dynamic modelling framework and optimal control strategy for virtually coupled train sets with guaranteed safety

Virtual coupling represents a novel railway operational mechanism, wherein several trains share state information and adjust their behaviours based on control objectives, which include safety (collision avoidance) assurance and stability (uniform velocity and constant spacing between trains) maintenance. However, the conflict between safety and stability requirements becomes more apparent, as the closer spacing between trains. Moreover, the hybrid nature of the train platoon operation, which involves both discrete events and continuous dynamics, also poses a significant barrier to the control strategy synthesis. This paper presents the Virtually Coupled Train Sets (VCTS) dynamic modelling framework and strategy synthesis approach to address these problems. A Hybrid Markov Decision Process (HMDP) is adopted for relative longitudinal dynamics modelling of trains, considering the influence of the uncertainty dynamic of trains ahead. A Stackelberg game-based strategy synthesis algorithm is applied for the safe guarantee by overcoming the uncertainty, and a Q-learning method is used for stability strategy selection from the safe strategy. The results showed a 17.95% expansion in the safe state space when compared to the general relative braking scheme (which assumes maximal acceleration during the delay horizon). In a four-train tracking operation scenario, compared with Q-learning without safe constraints, our approach gives similar stability performance; compared with Q-learning with general relative braking safe constraints, our approach not only assurance safety but has a better stability performance.

BCIDS‐IoT: A Binary Classification Intrusion Detection Scheme for Internet of Things Communication

ABSTRACTThe integration of Internet of Things (IoT) devices into daily life has exponentially increased the amount of data. In an IoT computing environment, like Smart Homes, Internet of Medical Things, Industrial Internet of things, Internet of Vehicles, and Smart Agriculture, there is a significant volume of data being exchanged between devices, servers, and users. This gives attackers a chance to launch malicious attacks on devices and associated resources. In this article, we have addressed this issue and proposed a machine learning‐based malware detection technique for the secure communication of IoT (BCIDS‐IoT). The proposed BCIDS‐IoT employs numerous algorithms for efficient detection. The benchmark UNSW‐NB15 dataset is utilized for the analysis. BCIDS‐IoT lowers false positives, maintains high detection rates, and allows for large‐scale network traffic without compromising performance. The various models, such as logistic regression, decision trees, random forests, extra trees, K‐nearest neighbors, and artificial neural network (ANNs), are utilized in the proposed BCIDS‐IoT. Metrics like precision, recall, and F1‐score are also calculated alongside accuracy. ANN surpassed all other models with an accuracy of . Finally, the proposed BCIDS‐IoT is also compared with different closely related schemes, indicating its outperformance among all.

Tighter bound for generalized multiple discrete logarithm problem via MDS matrix method

Discrete logarithm problem (DLP) is one of the fundamental hard problems used in cryptography. For 1≤k≤n, solving the k-out-of-n DLP instances is an important problem emerging in certain scenarios in public-key cryptography. Ying and Kunihiro (ACNS 2017) pioneered in studying k-out-of-n instance solutions of DLP, which is a generalized version of multiple DLP. By reducing the multiple DLP to the generalized version, they established lower bounds on the computational complexity of k-out-of-n DLP for different parameter values of k.In this paper, we further reduce the reduction complexity presented in Ying and Kunihiro's work and increase the range of k and n for the tight lower bound of k-out-of-n DLP in the generic group model, which has applications in related cryptographic schemes. To achieve the goal, the key technique is to utilize a variant of fast multipoint evaluation. We divide the discussion into two cases. In the special case when n divides p−1, by leveraging Number Theory Transform (NTT) technique, we expand k and n to a larger range. In the general case, by using a variant of fast multipoint evaluation, we increase k and n to a moderately larger range.

Performance analysis and optimization of a vehicular LT-PEMFC for maximum power density and efficiency based on NSGA-II algorithm

Based on finite temporal thermodynamics (FTT), a model for a low-temperature proton exchange membrane fuel cell (LT-PEMFC) is created. The link between the LT-PEMFC’s output power density and efficiency was determined using the aforementioned model. The experimental results confirm the algorithm model’s accuracy. Investigations are also conducted into how the design and operating parameters affect the LT-PEMFC’s output outcomes. Moreover, the NSGA-II technique is used to maximize the power density and efficiency of the LT-PEMFC for multi-objective optimization. The experiment outcomes demonstrate that the optimized LT-PEMFC model performs better at output. The related powertrain design scheme for the various output performances of Fuel cell vehicles (FCVs) is provided by LT-PEMFC optimization. It is demonstrated by the study of simulation data that the optimized LT-PEMFC achieves reduced hydrogen consumption and higher FCV efficiency.

ЩОДО ОДНОГО ПІДХОДУ ДО ВИРІШЕННЯ ПИТАННЯ АВТОМАТИЗОВАНОГО БУДУВАННЯ СХЕМИ ПОСИЛАНЬ РЕЛЯЦІЙНИХ БАЗ ДАНИХ

Introduction. The current trend of continuous integration of various areas of scientific and practical human activity contributes to the constant increase in the complexity of problems, the successful solution of which becomes possible only through the use of large volumes of information, as a rule, geographically distributed across multiple database files. In this case, the need to know the locations and features of the interaction of its components becomes of fundamental importance when organizing the most convenient and efficient ways to access it. In some cases, to meet such needs, you can use the services of existing products (Microsoft Access, Microsoft SQL Server, MySQL, FileMaker Pro, Oracle Database, dBASE, etc.). Sometimes, using a specialized SQL language or its dialects, it is more convenient to create a “handmade” one that will satisfy the stated requirements. If the user desires or needs to know the location of the information used in the work, understand the logic of the mechanism for obtaining it, and evaluate the effectiveness of the ways of its delivery to constantly monitor its current and future nomenclature and quantity, its developments can help, which can provide more detailed information, than known means. As experience shows, obtaining an integral and effective solution to this set of closely interrelated issues becomes possible only with the availability of a purely specialized software tool for the automated construction of link diagrams, which is a multifunctional, operational, easy-to-learn, and easy-to-use user environment. Endowed with the ability at any time and with minimal user participation to visualize on the screen the current structure of the created database with the required degree of clarity, detail, and ease of perception, such an environment should become a means of operational analysis and assessment of ways of possible modification of the database itself in the direction of creating a more effective one in practical terms, regarding the variant of its structure. Purpose. Research and practical testing of the existing capabilities of approaches, methods, algorithms, and software modules that were developed during the creation of a universal information software technology for working with undirected graphs of the “mixed forest” type. To evaluate the efficiency and feasibility of using these approaches to improve a set of integrated software tools for the automated construction of relational database link diagrams with minimal user participation in this process. Methods. Database systems, apparatus of graph-tree theory. Result. A new approach is proposed, and the result of its software implementation is presented in the form of a “pilot” version of a set of software tools for the automated construction of relational database link schemes. The basis of the approach is techniques, methods, algorithms, etc. which were created and implemented programmatically in a new, universal information software technology for working with (temporarily) undirected graphs of the “sour cream forest” type. , Software package (SP) makes “formal” the user’s participation in the process of building link diagrams. By greatly reducing the time required for this and providing more than 90% savings in the technical resources of the computer used, SP makes it possible to take into account data tables, the total number of fields of which is limited only by the latter’s capabilities. A positive conclusion about the suitability of such an approach will become an incentive for its further improvement in the form of specialized information software technology as an effective means of solving the problems of constructing relational database link schemes.

Relational Maneuvering of Leader-Follower Unmanned Aerial Vehicles for Flexible Formation.

In this article, we propose a new formation scheme for a leader-follower unmanned aerial vehicle (UAV) system inspired by a human pilot's behavior wherein the formation geometry does not necessarily remain fixed as the vehicles maneuver. In other words, the position and the orientation of the follower with respect to the leader are subject to change as they maneuver while satisfying some constraints. Our strategy ensures that the follower UAV maintains a desired fixed relative distance with respect to the leader UAV, whereas its orientation with respect to the leader UAV may change to reduce its control effort and provide it with a tactical advantage. We call this new relational maneuvering scheme flexible since the set of feasible positions for the follower UAV is not fixed, as is common in close proximity two-ship formations in air-to-air combat. By assigning the follower UAV's linear and angular velocities as its control inputs, our approach tries to emulate a human pilot's behavior in UAVs by taking anticipatory maneuvers when the leader UAV makes aggressive turns. The proposed flexible-geometry formation scheme is robust to the leader's maneuver changes since the follower UAV's control law does not need the information of the leader's angular speed control and only uses relative measurements. This makes the design lucrative even when the vehicles are heterogeneous, global measurements are unavailable, or if the leader UAV is noncooperative. Finally, we present multiple simulations to highlight the merits of the flexible formation control laws.

Overall performance assessment of selected small-scale irrigation schemes using internal and external performance indicators in West Hararghe Zone, Eastern Ethiopia

This study was conducted to evaluate the overall performance of selected small-scale irrigation schemes using internal and external performance indicators. The performance of the irrigation schemes in relation to water balance was evaluated using three indicators. The results indicated that the relative water supply (RWS), relative irrigation supply (RIS), and field application ratio (FAR) of Midagdu irrigation scheme were 0.9, 1.3, and 0.75 respectively. This indicates that the water supply is not closely related to the water demand. However, for the Wadeti Irrigation scheme, the RWS, RIS, and FAR are 0.48, 0.35, and 2.83, respectively, indicating water stress. The Midagdu irrigation scheme shows 7496.75 birr/ha for both output per unit command area and output per unit cropped area. Output per unit irrigation water supply was 12.5 birr/m3, while output per unit water consumed was 4.4 birr/m3. For the Wadeti irrigation scheme, the values were 11,276.12 birr/ha, 11,276.12 birr/ha, 8.7 birr/m3, and 1.6 birr/m3, respectively. This study results showed that the Wadeti irrigation scheme is more land-efficient, while the Midagdu scheme is more water-efficient. The values of the hydraulic performance indicators Conveyance Efficiency (Ec), Water delivery capacity (WDC), and Delivery Performance Ratio (DPR), for the Midagdu irrigation scheme were 75 %, 0.45, and 0.8, respectively, while for the Wadeti irrigation scheme, the values were 42 %, 0.11, and 0.4, respectively. These results indicate that the water delivery performance of the two schemes was poor. Therefore, improving the hydraulic performance of the scheme requires minimizing water conveyance losses.

Diagnostic analystics in the Bayesian vector autoregressive model

The vector autoregressive model is extensively employed in macroeconomics, finance, and the natural sciences. However, it often encounters the issue of over-parametrization, resulting in undesirable behavior within the model. In this paper, we utilize the Bayesian vector autoregressive model as a tool to tackle this problem. Additionally, we employ the Bayesian local influence method to detect possible extreme observations under the model. We use posterior inference to estimate related parameters and construct Bayesian perturbation schemes for priors, variance, and data perturbations. Bayesian local influence is conducted based on three objective functions: Bayes factor, ϕ divergence, and posterior mean. To demonstrate the effectiveness of the diagnostics, we conduct numerical simulations. In the real data example, we use US inflation, unemployment, and interest rate data to verify this method's effectiveness.

Robust secret color image sharing anti-cropping and tampering in shares

Secret image sharing (SIS) has excellent properties such as loss tolerance and relatively low computational complexity, providing a brand-new solution for image security protection. However, there has been little research on the robustness of SIS systems, such as how to resist cropping or malicious tampering in shares. Existing related schemes generally focus on grayscale images and suffer from issues such as lossy recovery, weak robustness, and serious pixel expansion, which are challenging to meet the requirements of high-quality sensitive image applications. In this regard, a robust SIS scheme for color images is proposed, which can resist large-scale cropping and malicious tampering in shares. According to the idea of “breaking up the whole cropped area into parts, repairing the shares independently”, the proposed scheme can realize lossless recovery of secret images through organic fusion of secret sharing, error-correcting code, and pixel re-arrangement techniques. Even if all the shares are cropped by 25%, it can still achieve lossless recovery regardless of whether the cropping positions intersect or overlap. It can also resist various malicious tampering (such as marking, defacing, and copy-move forgery) as well as image noise. Moreover, it avoids pixel expansion and requires no auxiliary encryption or preprocessing. Theoretical analysis and experimental results demonstrate that the proposed scheme is superior to existing schemes in terms of robustness and comprehensive performance, and is expected to promote the practical application of SIS.

Emotional schemas in relation to educators’ social and emotional competencies to promote student SEL

A rapidly growing body of research examines the social and emotional competencies (SEC) educators need to fulfill their professional roles effectively. The prosocial classroom model emphasizes the significance of educators' SECs in maintaining wellbeing, managing classrooms, building positive student relationships, and delivering proficient social-emotional learning (SEL) instruction. This paper examines the relationship between emotional schemas and educators’ SEC, exploring their relevance in understanding developmental contexts for both educators and students. Effective SEL instruction requires educators to model the skills they teach, manage stress and emotional reactivity, and provide “co-regulation” to help students manage their emotions. Despite the importance of these competencies, research has yet to fully clarify the constructs contributing to educators' SECs and how cultural variations in emotion understanding affect this dynamic. Emotional schemas, formed through developmental processes and socialization, influence psychological well-being, impact relationships, and can be adaptive or maladaptive. Theories of meta-emotion and metacognition provide frameworks for understanding and developing adapticas, their wellbeing, and their capacity to create prosocial classroom environments. We examine existing intervention strategies that support adaptive emotional schemas, aiming to improve educators' SECs, SEL instruction, and overall well-being. Finally, we propose directions for future research to enhance understanding and practical applications in educational settings.

Distributed Identity Authentication with Lenstra-Lenstra-Lovász Algorithm-Ciphertext Policy Attribute-Based Encryption from Lattices: An Efficient Approach Based on Ring Learning with Errors Problem.

In recent years, research on attribute-based encryption (ABE) has expanded into the quantum domain. Because a traditional single authority can cause the potential single point of failure, an improved lattice-based quantum-resistant identity authentication and policy attribute encryption scheme is proposed, in which the generation of random values is optimized by adjusting parameters in the Gaussian sampling algorithm to improve overall performance. Additionally, in the key generation phase, attributes are processed according to their shared nature, which reduces the computational overhead of the authorization authority. In the decryption phase, the basis transformation of the Lenstra-Lenstra-Lovász (LLL) lattice reduction algorithm is utilized to rapidly convert shared matrices into the shortest vector form, which can reduce the computational cost of linear space checks. The experimental results demonstrate that the proposed method not only improves efficiency but also enhances security compared with related schemes.

CoinFA: an efficient coin mixing scheme with flexible amounts

Abstract Coin mixing is an efficient anonymization technology of cryptocurrency used to eliminate the linkability of transaction parties by hiding their addresses in an anonymous set. However, a common weakness with most existing coin mixing schemes is that the amount of mixed coins must be the same for all requests within a mixing cycle, otherwise it is easy for an attacker to restore the linkability of transaction parties. In this paper, we design a stage-payable puzzle solution mechanism, named CoinFA, which reverses the control of the requesting amounts to the users for flexible mixing amounts. In our design, the payee (with an output address) first requests a puzzle from the mixers and the latter are the only ones who know the solution of the puzzle. If the payee solves the puzzle successfully, he can be rewarded with the corresponding Bitcoins. The payer (allowed to have multiple input addresses) then requests the solution by paying in installments. We achieve better security by weakening the rights of the involved third parties, while the hierarchical structure allows our solution to have better efficiency and robustness. We perform a security analysis on CoinFA based on the standard Rivest-Shamir-Adleman (RSA) assumption and Elliptic Curve Digital Signature Algorithm unforgeability. We also analyze the performance of CoinFA by comparing it with two related schemes, and the results show that our CoinFA scheme has a greater advantage when the mixing amount is relatively small.

Enhancing Healthcare Informatics: Integrating Category Theory Reasoning into OMOP-CDM Ontology Model.

The task of managing diverse electronic health records requires the consolidation of data from different sources to facilitate clinical research and decision-making support, with the emergence of the Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) as a standard relational database schema for structuring health records from different sources. Working with ontologies is strongly associated with reasoners. Implementing them over expansive and intricate Ontologies can pose computational challenges, potentially resulting in slow performance. In this paper, we propose the implementation of a new reasoner based on categorical logic over a translation of OMOP-CDM into an ontology model. This enables enhancements to the efficiency and scalability of implementing such models.

EMAKAS: An efficient three-factor mutual authentication and key-agreement scheme for IoT environment

The adoption of IoT in healthcare revolutionizes remote patient monitoring and healthcare efficiency. Yet, it brings notable security and privacy challenges, particularly in resource-constrained environment. We propose a secure and efficient three-factor lightweight mutual authentication and key agreement scheme, designed for IoT-based smart healthcare systems, addressing these critical concerns. The scheme employs a fuzzy extractor, a one-way hash function, Elliptic Curve Discrete Logarithm and XOR operations for efficient cryptographic transformations, creating a robust framework for secure data handling. The scheme's design focuses on security and privacy while minimizing computational demands, making it ideal for resource-constrained IoT devices. We utilized both informal and formal security analyses to validate our scheme, employing the Random Oracle Model (ROM), Scyther tool and Burrows-Abadi-Needham (BAN) logic. The security and performance analysis showed that our scheme offers more security features across 15 defined criteria with minimal communication and computational costs compared to other related schemes. The scheme is not only robust against security threats but also practical for implementation in IoT healthcare environment, offering a solution for secure IoT communication by achieving mutual authentication and key agreement with minimized computational requirements.