Alternative Approach Research Articles

Dopaminergic Cell Replacement for Parkinson's Disease: Addressing the Intracranial Delivery Hurdle.

Parkinson's disease (PD) is an increasingly prevalent neurological disorder, affecting more than 8.5 million individuals worldwide. α-Synucleinopathy in PD is considered to cause dopaminergic neuronal loss in the substantia nigra, resulting in characteristic motor dysfunction that is the target for current medical and surgical therapies. Standard treatment for PD has remained unchanged for several decades and does not alter disease progression. Furthermore, symptomatic therapies for PD are limited by issues surrounding long-term efficacy and side effects. Cell replacement therapy (CRT) presents an alternative approach that has the potential to restore striatal dopaminergic input and ameliorate debilitating motor symptoms in PD. Despite promising pre-clinical data, CRT has demonstrated mixed success clinically. Recent advances in graft biology have renewed interest in the field, resulting in several worldwide ongoing clinical trials. However, factors surrounding the effective neurosurgical delivery of cell grafts have remained under-studied, despite their significant potential to influence therapeutic outcomes. Here, we focus on the key neurosurgical factors to consider for the clinical translation of CRT. We review the instruments that have been used for cell graft delivery, highlighting current features and limitations, while discussing how future devices could address these challenges. Finally, we review other novel developments that may enhance graft accessibility, delivery, and efficacy. Challenges surrounding neurosurgical delivery may critically contribute to the success of CRT, so it is crucial that we address these issues to ensure that CRT does not falter at the final hurdle.

Sequence-based malware detection using a single-bidirectional graph embedding and multi-task learning framework

As an important part of malware detection and classification, sequence-based analysis can be integrated into dynamic detection system for real-time detection. This work presents a novel learning method for malware detection models that leverages advances in graph embedding for fusing the n-gram data into a one-hot feature space with different transmission directions. By capturing the information flow, our method finds a better feature representation for detection tasks with rely solely on sequence information. To enhance the stability of feature representation, this work adopts a multi-task learning strategy which achieves better performance in independent testing. We evaluate our method on two different realworld datasets and compare it against four superior malware detection models. During malware detection using our method, we conducted in-depth discussions on feature length, graph embedding direction, model depth, and different multi-task learning strategies. Experimental and discussion results show that our method significantly outperforms alternative approaches across evaluation settings.

Understanding What in Public Understanding of Science

Abstract What should citizens understand about science to participate in democratic life? Against the prevailing approach, we argue that “what” a public understanding of science is about strongly depends on the specific epistemological nature of the science related issues considered in different contexts and circumstances. We identify three specific categories of such issues and show how, equally, specific models of public understanding are required to address them. Only by endorsing such an alternative approach will citizens arguably be able to form sound opinions about those very issues, as well as to discuss and deliberate rationally about them.

Data-Driven Linear Quadratic Optimization for Controller Synthesis With Structural Constraints.

For various typical cases and situations where the formulation results in an optimal control problem, the linear quadratic regulator (LQR) approach and its variants continue to be highly attractive. In certain scenarios, it can happen that some prescribed structural constraints on the gain matrix would arise. Consequently then, the algebraic Riccati equation (ARE) is no longer applicable in a straightforward way to obtain the optimal solution. This work presents a rather effective alternative optimization approach based on gradient projection. The utilized gradient is obtained through a data-driven methodology, and then projected onto applicable constrained hyperplanes. Essentially, this projection gradient determines a direction of progression and computation for the gain matrix update with a decreasing functional cost; and then the gain matrix is further refined in an iterative framework. With this formulation, a data-driven optimization algorithm is summarized for controller synthesis with structural constraints. This data-driven approach has the key advantage that it avoids the necessity of precise modeling which is always required in the classical model-based counterpart; and thus the approach can additionally accommodate various model uncertainties. Illustrative examples are also provided in the work to validate the theoretical results.

Green Synthesis and Antibiofilm Activity of Silver Nanoparticles by Camellia sinensis L. (White Tea Leaf)

Biosynthesis of nanoparticles refers to the production or synthesis of nanoparticles using organisms, such as bacteria, fungi, plants or their byproducts. This approach offers several advantages over conventional chemical methods, including eco-friendliness, cost-effectiveness, and potential for large-scale production. The silver nanoparticles (AgNPs) synthesized using aqueous Camellia sinensis L. (white tea leaf) extracts as reducing and stabilizing agents were reported and evaluated for antibiofilm activity against test microorganisms (Acinetobacter baumanii ATCC 19606 NRRLB 3704, Pseudomonas aeruginosa ATCC 27853 (Gram –), and Bacillus subtilis ATCC 6633, Staphylococcus haemolyticus ATCC 43252 (Gram +), and Candida albicans ATCC 10231) in the study. The synthesized AgNPs were observed and characterized using Uv-Vis spectroscopic analysis, scanning electron and transmission electron microscopy energy-dispersive spectra and Fourier transform infrared spectroscopy. The synthesized AgNP was also screened for antibiofilm activity against test microorganisms. Our results show that the synthesized AgNPs have the potential to be used for antibiofilm materials and different biomedical applications.

Direct Stiffness–Strength Method: An Alternative Design Approach to AISI for Sheathed Cold-Formed Steel Z-Section Structural Members Subjected to Bending

Direct Stiffness–Strength Method: An Alternative Design Approach to AISI for Sheathed Cold-Formed Steel Z-Section Structural Members Subjected to Bending

Effectiveness of Using a Self-Directed Learning Program to Teach Physical Examination and Health Assessment Skills: A Quasi-Experimental Study.

Conducting health assessments and physical examinations are essential skills for independent practice. Exploring how to teach these skills effectively is essential. This study was designed to validate the effectiveness of a self-directed learning program in improving nursing student outcomes in terms of their learning health assessment and physical examination skills. A quasi-experimental design was used, and second-year nursing students enrolled in a health assessment and physical examination course at a university of science and technology in central Taiwan were recruited using purposive sampling. Both the control and experimental groups were taught using a traditional teaching method. The experimental group additionally participated in a self-directed learning program. Structured questionnaires, including the Chinese version of the Self-Directed Learning Readiness Scale, Scales of Motivation and Learning Strategies, Interpersonal Skills Assessment Tool, the Course Satisfaction Evaluation Scale, and the Perception of Health Assessment and Physical Examination Competence Scale, were used to collect data at three time points. After controlling for the effects of pretest scores, after the intervention, the experimental group achieved significantly higher scores than the control group for self-directed learning (creative learning and love of learning), learning motivation (goal orientation, work value, expected success, and test anxiety), and cognitive strategies (total score, elaboration strategy, recitation strategy, and monitoring strategy). In addition, the health assessment exercise improved interpersonal and communication skills, and learning satisfaction was significantly higher in the experimental group than the control group. A physical examination and health assessment course designed using the self-directed learning framework can effectively improve student competence in related skills to enhance their ability to assess patient health problems in clinical settings. This study presents an alternative approach to teaching health assessment and physical examination courses and validates the positive effect of this approach on student learning outcomes.

Applying an ecological approach to practice design in American football: some case examples on best practice

ABSTRACT In this paper, we outline an ecological approach to practice design in American football to support coaches in helping players to coordinate skilled movement behaviours in dynamic performance environments. This approach may require moving away from some long-held practice approaches traditionally employed by some coaches across all performance levels. To guide this progression, we present two novel case examples to support coaches interested in moving towards more contemporary pedagogical frameworks that support the notion of their role as a practice designer, centralising athlete-environment interactions. Distinctively, through the utilisation of a constraints-led methodology, coaches could design practice tasks to offer opportunities for players to interact with challenging performance problems. Our case examples range from high school players to National Football League standouts to support the implementation of alternative approaches to practice design, exploring what an ecological dynamics rationale could look, feel and sound like in the context of American football.

Correcting for cutoff dependence in backward evolution of QCD parton showers

Monte Carlo event generators for hard hadronic collisions depend on the evolution of parton showers backwards from a high-scale subprocess to the hadronization scale. The evolution is treated as a branching process with a sequence of resolvable parton emissions. The criterion of resolvability involves cutoffs that determine the no-emission probability (NEP) for a given range of the evolution scale. Existing event generators neglect cutoff-dependent terms in the NEP that, although formally power-suppressed, can have significant phenomenological effects. We compute such terms and study their consequences. One important result is that it is not possible for the backward shower to faithfully reproduce the cutoff-independent parton distribution functions (PDFs) used to generate it. We show that the computed NEP corrections mitigate but do not eliminate this problem. An alternative approach is to use cutoff-dependent PDFs that are consistent with the uncorrected NEP. Then one must apply cutoff-dependent corrections to hard subprocess matrix elements. We compute those corrections to the first nontrivial order for the Drell-Yan process and for Higgs production by gluon fusion.

Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation

Sign language provides a way for differently-abled individuals to express their feelings and emotions. However, learning sign language can be challenging and time consuming. An alternative approach is to animate user photos using sign language videos of specific words, which can be achieved using existing image animation methods. However, the finger motions in the generated videos are often not ideal. To address this issue, we propose the Structure-aware Temporal Consistency Network (STCNet), which jointly optimizes the prior structure of humans with temporal consistency to produce sign language videos. We use a fine-grained skeleton detector to acquire knowledge of body structure and introduce both short- and long-term cycle loss to ensure the continuity of the generated video. The two losses and keypoint detector network are optimized in an end-to-end manner. Quantitative and qualitative evaluations on three widely used datasets, namely LSA64, Phoenix-2014T, and WLASL-2000, demonstrate the effectiveness of the proposed method. It is our hope that this work can contribute to future studies on sign language production.

The kernels of powers of linear operator via Weyr characteristic

The adjoint of a matrix in the Lie algebra associated with a matrix algebra is a fundamental operator, which can be generalized to a more general operator φ AB : X → AX − XB by two matrices A and B. The kernel of the operator is very well-known and it can be found in Gantmacher's book. The formulas for the dimensions of the kernels of arbitrary powers of the operator φ AB were given in terms of the Segre characteristics of these two matrices by the second and third authors in this paper and their collaborators. This paper provides an alternative approach to this problem via the Weyr characteristic in a more essential method. We obtain formulas for the dimensions of the kernels of arbitrary powers of the operator in terms of the Weyr characteristics. Furthermore, the basis for kernel of each power of the operator is described explicitly. As a consequence, for arbitrary square matrices A and B over an algebraically closed field, the dimension of the kernel of each power of the operator φ A − λI , B for eigenvalues λ of φ AB can be viewed as a similarity invariant of the operator φ AB , so we characterize the operator within similarity, which should be of interest to a number of people (including physicists).

Лайфхаки глокализации, или Глокализация «лайфхака»

The article discusses the process of glocalization of the English-language concept “lifehack” in the Russian language on the basis of corpus data. Both original and borrowed concepts are considered in the Internet discourse, which is currently the most accessible source for studying new concepts and at the same time, the first to incorporate them into its language. The purpose of the article is to identify cognitive features of the “lifehack” concept in the English language and its Russian counterpart, as well as the changes that have occurred as a result of its glocalization. Based on collocation analysis and discursive analysis of language units which represent the concept, cognitive features are defined and combined into thematic groups. The totality of these features makes up the field structure of the concepts. The study revealed that the core features of the “lifehack” concept were fully integrated into the Russian language version of the concept. These include thematic groups such as “use”, “help”, and “ease/simplicity”. In both the Russian and English versions, the concept received a large number of positive characterizations (“good”, “great”, “incredible”, etc.). Cognitive features “shallowness” and “negative evaluation” can be found in the periphery of the concept. Additionally, features that were not present in the original concept have been added to the concept in the Russian language. The grammatical structures in which the words representing the concept “lifehack” are used suggest that the adaptation process is ongoing. In the process of glocalization, the “lifehack” concept in the Russian language retained all the core features of the original concept, while also acquiring an additional feature “alternative approach”. The grammatical characteristics of the language units representing the concept in the Russian language indicate that it is still undergoing adaptation and, possibly, will further develop in a bit different direction from the original concept.

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.

Text-to-Image Generation for Abstract Concepts

Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually directly associated with physical objects, expressing abstract concepts through natural language requires considerable effort since they are characterized by intricate semantics and connotations. An alternative approach is to leverage images to convey rich visual information as a supplement. Nevertheless, existing Text-to-Image (T2I) models are primarily trained on concrete physical objects and often struggle to visualize abstract concepts. Inspired by the three-layer artwork theory that identifies critical factors, intent, object and form during artistic creation, we propose a framework of Text-to-Image generation for Abstract Concepts (TIAC). The abstract concept is clarified into a clear intent with a detailed definition to avoid ambiguity. LLMs then transform it into semantic-related physical objects, and the concept-dependent form is retrieved from an LLM-extracted form pattern set. Information from these three aspects will be integrated to generate prompts for T2I models via LLM. Evaluation results from human assessments and our newly designed metric concept score demonstrate the effectiveness of our framework in creating images that can sufficiently express abstract concepts.

The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models

Thompson sampling (TS) has been known for its outstanding empirical performance supported by theoretical guarantees across various reward models in the classical stochastic multi-armed bandit problems. Nonetheless, its optimality is often restricted to specific priors due to the common observation that TS is fairly insensitive to the choice of the prior when it comes to asymptotic regret bounds. However, when the model contains multiple parameters, the optimality of TS highly depends on the choice of priors, which casts doubt on the generalizability of previous findings to other models. To address this gap, this study explores the impact of selecting noninformative priors, offering insights into the performance of TS when dealing with new models that lack theoretical understanding. We first extend the regret analysis of TS to the model of uniform distributions with unknown supports, which would be the simplest non-regular model. Our findings reveal that changing noninformative priors can significantly affect the expected regret, aligning with previously known results in other multiparameter bandit models. Although the uniform prior is shown to be optimal, we highlight the inherent limitation of its optimality, which is limited to specific parameterizations and emphasizes the significance of the invariance property of priors. In light of this limitation, we propose a slightly modified TS-based policy, called TS with Truncation (TS-T), which can achieve the asymptotic optimality for the Gaussian models and the uniform models by using the reference prior and the Jeffreys prior that are invariant under one-to-one reparameterizations. This policy provides an alternative approach to achieving optimality by employing fine-tuned truncation, which would be much easier than hunting for optimal priors in practice.

The IPCC reports and HE Geography: opportunities lost and found

ABSTRACT Climate change education within UK HE Geography has not produced a common curriculum, despite having an obvious set of learning resources, in the form of the Intergovernmental Panel on Climate Change (IPCC) report series, which has in many ways structured knowledge and understanding on climate change, its impacts and mitigation globally for several decades. Use of the IPCC reports is devolved to module or course-unit level, and is very varied as a consequence. Examples of alternative approaches to supporting climate change learning with IPCC reports are briefly discussed, before attention is drawn to the relatively new, online Interactive Atlas that was released as part of the IPCC Sixth Assessment Report. This makes available key datasets from the Working Group 1 (The Physical Science Basis) section of the report. As a highly accessible climate GIS, the Atlas facilitates flexible spatial and temporal analyses of trends and changes in regional impacts that can greatly enrich learning and teaching in the geographical aspects of the intensifying climate crisis. Being entirely free to the end user, and not requiring any specialist IT equipment or training, the Atlas has major benefits, and some potential learning applications are suggested.

Abstract 3998: Preclinical efficacy of a synthetic Notch-based combinatorial immunotherapy against colorectal cancer with HER2 amplification

Abstract HER2 amplification occurs in about 5% of colorectal cancers (CRC) and is only partially associated with clinical response to combined HER2/EGFR targeted treatment. An alternative approach, based on Adoptive Cell Therapy (ACT) using T cells engineered with anti-HER2 Chimeric Antigen Receptor (CAR), proved toxic due to "on-target off-tumor" activity. Therefore, we developed a combinatorial ACT strategy to safely target HER2 amplification in CRC using a synNotch-based artificial regulatory network. A synthetic Notch receptor was employed in which the extracellular domain is an anti-HER2 scFv and the intracellular domain contains the GAL4VP64 artificial transcription factor. Engagement of the HER2-synNotch by overexpressed HER2 on the surface of target cells induces GAL4VP64 cleavage and translocation to the nucleus, where it drives expression of a CAR under a GAL4-responsive promoter. In this way, only cells co-expressing both HER2 and the CAR target are killed. Carcinoembryonic antigen (CEA) was selected as a colon-specific CAR target: CEA expression is indeed restricted to the digestive tract and is increased in CRC. The natural killer cell line NK-92 was chosen as effector to be engineered. NK-92 cells transduced with HER2-synNotch and inducible CEA-CAR lentiviral vectors were subsequently sorted in the OFF and ON state to select those with the higher CAR induction after synNotch engagement. Subsequently, cloning of sorted cells led to identification of an optimally responsive clone (clone 5F), in terms of specificity and amplitude of CAR induction. The 5F clone demonstrated significant activity in vitro and in vivo specifically against HER2amp CRC models, with no effects on cells with physiological HER2 levels. In vitro, the 5F clone displayed selective cytotoxicity against HER2amp/CEA+ CRC cells, with minimal killing activity against HER2amp/CEA- cells, or HER2norm/CEA+ cells. Additional assays on 3D organoids highlighted better recruitment and infiltration by the 5F clone respect to NK-92 WT cells, only in HER2amp models. In vivo, NK-92.5F significantly impaired tumor growth in two different HER2amp CRC models, with no effect on HER2norm CRC cells. To further improve survival, tumor penetration and in vivo efficacy of the NK-92.5F clone, a more complex system was built, in which HER2 synNotch engagement drives expression of both the CEA-CAR and IL2. 5F-IL2 cells displayed further increased cytotoxicity in vitro. In vivo, 5F-IL2 cells drastically increased survival of mice carrying HER2amp CRC xenografts with respect to the parental 5F clone. The observed selective therapeutic efficacy both in vitro and in vivo of this innovative HER2 synNotch/CEA-CAR system, and its future evolutions, opens perspectives for possible clinical applications in cases of HER2amp CRC resistant or partially responsive to HER2/EGFR blockade. Citation Format: Marco Cortese, Erica Torchiaro, Alice D'Andrea, Consalvo Petti, Federica Invrea, Letizia Franco, Chiara Donini, Valeria Leuci, Simonetta M. Leto, Valentina Vurchio, Francesca Cottino, Claudio Isella, Sabrina Arena, Elisa Vigna, Andrea Bertotti, Livio Trusolino, Dario Sangiolo, Enzo Medico. Preclinical efficacy of a synthetic Notch-based combinatorial immunotherapy against colorectal cancer with HER2 amplification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3998.

Abstract 2021: The RXR agonist MSU-42011 and the MEK inhibitor selumetinib reduce pERK levels in NF1-deficient cells and inhibit cytokine production in macrophages

Abstract Neurofibromatosis type 1 (NF1) is a common genetic disease that predisposes approximately 50% of affected individuals to develop plexiform neurofibromas (PNFs), which can progress to highly aggressive malignant peripheral nerve sheath tumors (MPNSTs) in approximately 10% of patients. NF1 is caused by mutations in the tumor suppressor gene NF1, which encodes for neurofibromin, a negative regulator of RAS activity. Selumetinib, a specific inhibitor of MEK1/2, is the only FDA-approved drug for NF1-associated PNFs. However, the anti-tumor effects of selumetinib are limited in MPNSTs and have dose-limiting side effects. Deficiency of the NF1 gene not only promotes tumorigenesis but also has broad effects on the immune cells and cytokine signaling driven by hyperactive RAS signaling. Because macrophages account for almost half of cells in NF1 lesions and their infiltration correlates with disease progression, we hypothesized that targeting tumor-promoting immune cells could be an alternative approach for treating NF1. The novel retinoid X receptor (RXR) agonist MSU-42011 reduces tumor growth in experimental Kras-driven cancers by decreasing pERK expression, reducing tumor-promoting immune cells like CD206+ macrophages and regulatory T cells, and increasing activated cytotoxic T cells. Here, we treated NF1-deficient cells and macrophages with MSU-42011 and selumetinib, either alone or in combination, using monoculture and conditioned media (CM) conditions. In human PNF cells and mouse MPNSTs, treatment with 200 nM MSU-40211 or 50 nM selumetinib for 3 hours reduced pERK protein levels compared with untreated controls, and the combination treatment enhanced this reduction in pERK protein levels. Additionally, there was a trend toward reduction in cell viability with increasing drug concentrations after 72 hours of the combination treatment. Moreover, CM from human and mouse PNF cells increased the mRNA expression of monocyte chemoattractant CCL2 (C-C motif chemokine ligand 2) and the secretion of IL-6 and TNFα in human THP1 monocytes/macrophages and bone marrow derived macrophages (BMDM). Notably, MSU-42011 and selumetinib alone inhibited CCL2 mRNA expression in THP1 macrophages and BMDM stimulated with CM from human and mouse PNF cells, respectively, and the inhibition of CCL2 mRNA expression was greatest with combination treatment. The combination of MSU42011 and selumetinib also significantly reduced tumor burden in a LL2 model of lung cancer driven by an activating Kras mutation. Based on the similarities in RAS activation and immune cell infiltration in NF1 and lung cancer, our next step is to confirm the immunomodulatory and anti-tumor effects of MSU-42011 and selumetinib in a syngeneic model of PNF and MPNST. Citation Format: Pei-Yu Hung, Jessica A. Moerland, Karen T. Liby. The RXR agonist MSU-42011 and the MEK inhibitor selumetinib reduce pERK levels in NF1-deficient cells and inhibit cytokine production in macrophages [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2021.

Weft-Knitted Strain Sensors for Motion Capture

Motion capture, especially of the knee angle, is an important component for situational triggering of functional electrical stimulation (FES). One major disadvantage of commercial FES devices is their bulky design that prevents unobtrusive wearing in everyday life and limits the patient’s free choice of appearance. This paper presents an alternative approach of sensors for motion capture in form of textile-based strain sensors. These can be integrated in a FES system in form of functional leggings, which make the FES system suitable for an unobtrusive daily use. Textile sensors, especially knitted sensors have already proven to be very promising to detect tensile strain. In particular, weft-knitted strain sensors, which can be integrated directly into the clothing during the knitting process, have the potential to detect the knee angle and therefore derive the gait phase due to the bending of the limbs without disturbing the wearer unnecessarily. Different designs of the weft-knitted strain sensor and their influence on the measurement behaviour of the sensor have been investigated. The weft-knitted strain sensor can be directly integrated in the knee area of the functional leggings to be used as a soft trigger to initiate electrical impulses for FES.

Enhancing mathematical simulation: a novel integro spline quasi-interpolation for nonlocal dynamical systems

ABSTRACT In this research, we present a novel and computationally streamlined method to tackle nonlocal initial value problems through a combination of modeling and simulation. Our approach utilizes the Integro spline quasi-interpolation technique, renowned for its effectiveness in approximating nonlocal integrals. To validate the efficacy of our proposed method, we conduct comprehensive comparisons with alternative approaches found in the existing literature for integrating nonlocal functions. Additionally, we successfully apply our suggested approach to address nonlocal Riccati, resistor-inductor circuit, and Airy oscillator dynamical systems. The numerical results obtained from these applications convincingly demonstrate the advantages and effectiveness of our developed approach in enhancing mathematical simulation.