General Architecture Research Articles

Inline Monitoring of continous Ultrasonic Welding Processes of Thermoplastic Composites via a custom polyCMUT based Ultrasound Array

Ultrasonic welding (UW) of thermoplastic composites (TCs) is an emerging technology in the field of composite joining techniques in the aerospace sector. Through a mechanical oscillator, ultrasound at a frequency of 20kHz is induced into the material via a welding horn, where microscopic friction and damping effects melt the thermoplastic. Under further pressure the weld area cools down, permanently joining both parts together. Like all joining processes in the aerospace industry the resulting joints need to be tested for their quality and structural integrity. The traditional testing method using water-coupled ultrasound includes extra steps. This process could be considerably improved by assessing the quality of the weld directly after or even during the welding process, allowing for immediate rework or discard of the parts in question. Ultrasound is still the best solution for this quality assessment, being inexpensive, well understood, and able to create B-Mode images, allowing a look into the cross-section of the weld. However, there are several major problems: To increase the system complexity as little as possible it is necessary to attach the ultrasound unit next to the welding equipment, and as close to the welding horn and compactor as possible to save space and keep the end- effector manoeuvrable. This brings problems for classic piezoelectric ultrasonic arrays: The low welding frequency and its resonance modes reach into the lower resonance modes of the piezoelectric sensors leading to immense noise, hiding any potential echo from the welding zone. Classic piezoelectric crystals are also very brittle and can suffer damage from sustained exposure to this violent environment. The authors present a novel solution: a custom-made polymer-based capacitive micromachined ultrasonic transducer array (polyCMUT). polyCMUTs are tiny drums with two electrodes. One on the bottom and the other suspended over a cavity sandwiched between two layers of polymers. By applying a DC-bias an electrical field is created and the membrane is set under tension. If then an AC voltage is applied, the strength of the electric field decreases, allowing the membrane to snap back into its original position. If done at the resonance frequency of the membrane, a strong ultrasonic signal is created. To receive this signal the polyCMUT is charged with a DC-bias, allowing it to receive the echo of the transmitted signal by measuring the changing capacitance. Not only is the polymer robust and inexpensive to fabricate, the general architecture of CMUTs also allows a design where the first mode of resonance is the actual mode the CMUT is operating in. By designing for a resonance frequency over 5 MHz all noise from the initial welding process is ignored, leading to a working pulse echo imaging system. The array is then mounted onto a PEEK block attached to the compactor unit of the welding end-effector. This publication is intended to present initial results, the design process of the custom array and the tests leading there.

Survey of Techniques and Applications in Object Recognition

Abstract: Object detection, which is closely related to video analysis and image comprehension, has received attention in years from researchers. Traditional approaches to object detection rely on crafted features and shallow trainable architectures. However, these methods face challenges. Often reach a plateau, in performance improvement. To overcome these limitations complex ensembles are often created by combining low-level image features with high-level context from object detectors and scene classifiers. The emergence of learning has introduced powerful tools that can learn semantics high level and deeper features. These tools address the limitations of architectures by introducing network structures training strategies and optimization functions. This paper provides a review of learning-based frameworks for object detection. The review starts with an overview of the history of learning with a particular emphasis on the Convolutional Neural Network (CNN) as an exemplary tool. It then delves into generic object detection architectures exploring modifications and effective strategies for improving detection performance. Recognizing the characteristics of detection tasks, the paper also briefly surveys several tasks such, as salient object detection, face detection, crowd analysis, and pedestrian detection.

A Generic High-Performance Architecture for VPN Gateways

Virtual private network (VPN) gateways are widely applied to provide secure end-to-end remote access and to relay reliable interconnected communication in cloud computing. As network convergence nodes, the performance of VPN gateways is limited by traditional methods of packet receiving and sending, the kernel protocol stack and the virtual network interface card. This paper proposes a generic high-performance architecture (GHPA) for VPN gateways in consideration of its generality and performance. In terms of generality, we redesign a generic VPN core framework by modeling a generic VPN communication model, formulating generic VPN core technologies and presenting corresponding core algorithms. In terms of performance, we propose a three-layer GHPA for VPN gateways by designing a VPN packet processing layer based on a data plane development kit (DPDK), implementing a user space basic protocol stack and applying our proposed generic VPN core framework. On the basis of the research work above, we implement a high-performance VPN (HP-VPN) and a traditional VPN (T-VPN) that complies with GHPA and traditional methods, respectively. Experimental results prove that the performance of HP-VPN based on GHPA is superior to T-VPN and other common VPNs in RTT, system throughput, packet forwarding rate and jitter. In addition, GHPA is extensible and applicable for other VPN gateways to improve their performance.

A unified digital twin approach incorporating virtual, physical, and prescriptive analytical components to support adaptive real-time decision-making

Based on an overview of the historical and rapidly expanding literature on digital twins, this paper identifies fundamental capabilities that outline a general and adaptable twin that supports system development, real-time interactions, prescribing courses of action, and actualizing them. We relate these capabilities to business analytics concepts and decision-making processes geared toward rapid adaptation to changing situations. This leads to a general digital twin architecture supporting a system throughout its lifecycle implemented with components including Internet of Things (IoT) devices, a virtual reality environment, network communications, and an analytic simulation. The success of this architecture revolves around an authoritative data source, the Highly Integrated Virtual Environment (HIVE). We demonstrate the architecture and capabilities through a transporter system example. This demonstration highlights important timing and synchronization questions critical to fulfilling the twin’s fundamental role of reacting to evolving real-world conditions. It identifies the importance of lags in decisions, relates it to prescriptive response time and the rate of evolution of the underlying system, and quantifies this impact with new metrics of effectiveness lag and relevancy decay.

Influence of milk phospholipids on microstructural changes in rat liver under tetracycline-induced hepatosis

The growing number of cases of adverse effects of medicines on the liver leads to hepatopathology and the development of complications such as cirrhosis, necrosis, liver failure, and liver carcinoma. Therefore, the purpose of this study was to determine the characteristic structural changes in the liver of rats with tetracycline damage and to find out the corrective effectiveness of milk phospholipids. The study performed histological examination of sections from different parts of the liver in experimental rats, which were stained with haematoxylin and eosin according to the conventional method. It was found that in case of artificial modelling of tetracycline-induced hepatosis in rats, the general architecture of the liver is preserved. At the same time, large-scale damage to hepatocytes and the development of fatty and granular dystrophy were recorded in the affected animals. Some of the damaged cells were destroyed, followed by lysis of fragments of the destroyed cells. The described microscopic changes were most pronounced in the areas of the liver under its capsule. As a result, there was partial or complete disorganisation of the liver lobes in all lobes of the organ. The use of milk phospholipids in the form of a biologically active additive “FLP-MD” as a corrective therapy in sick rats prevented the development of hepatocyte dyscomplexity, contributed to a substantial reduction in the count of destroyed cells in a state of dystrophy with isolated cases of disordered organisation of the liver laminae. This suggests a pronounced stimulatory effect of milk phospholipids on the processes of hepatocyte regeneration and repair in tetracycline-induced liver damage in rats. In case of separate administration of a milk phospholipid-based supplement to clinically healthy animals, the microscopic structure of liver samples did not differ from that of the control group. The findings of this study are of practical value for pathologists, clinicians, and therapists in case of development of drug-induced hepatopathology in animals, especially when using tetracycline antibiotics, and for determining the strategy in the treatment of such patients

Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario

An L3 smart vehicle (L3 SV) could behave differently from human-driven vehicles due to its intelligent configuration and decision-making logic, and may consequently exert influences on traffic flow. In order to clarify the L3 SV’s traffic impacts and accelerate L3 SV implementation, this paper conducted an evaluation on the traffic efficiency and energy consumption influences of L3 SVs based on a microscopic traffic simulation. Taking Beijing as a case, the relevant traffic economic benefits were calculated with the help of the previously proposed traffic economic benefits model. Before modeling L3 SVs, a two-dimensional general modeling architecture for SVs was proposed. According to the architecture, the driving behavior model, as well as behavior selection models of L3 SVs, was eventually determined, based on which the intelligent driving model of L3 SVs was established. Urban expressways were selected as the simulation road type, and scenario analysis was conducted on various proportions of L3 SVs and L3 connected and autonomous vehicles (L3 CAVs), as well as the input traffic flow rates. It was found that L3 SVs can significantly reduce the travel time and energy consumption and enlarge the actual road capacity. The improvement will become particularly prominent under saturated or supersaturated traffic flow and increasing the proportion of L3 SVs and L3 CAVs can also amplify the effect of traffic optimization. The related economic benefit is considerable, which is CNY 3.104 billion a year based on Beijing’s travel and traffic conditions.

A numerical simulation and analysis of chalcogenide BaZrS3-based perovskite solar cells utilizing different hole transport materials

Recently, barium zirconium sulfide (BaZrS3) chalcogenide perovskite (CP) material has attracted much attention in the photovoltaic community because of its excellent light harvesting ability, stability, and nontoxicity. BaZrS3 has shown great potential in becoming the best alternative to hybrid halide perovskites. Herein, we have numerically simulated and optimized the performance of a device with the general architecture FTO/WS2/BaZrS3/HTL/Au, using SCAPS-1D (version 3.3.07) software. In this general device, inorganic CuSCN, Cu2O and organic poly(3-hexylthiophene) (P3HT) were inserted, and performance was evaluated as hole transport layer (HTL) materials. Moreover, the influence of various parameters on the device, such as the effect of changing the absorber defect density, varying the operation temperature, and using different metal back contacts, were examined. The best HTL material was organic P3HT, and it exhibited a power conversion efficiency (PCE) of 13.86 %, with a Fill factor (FF) of 11.97 %, open circuit voltage (Voc) of 6.58 V, and current density (Jsc) of 17.59 mA cm−2. The other tested inorganic CuSCN and Cu2O HTL showed a PCE of 13.83 and 13.70 %, respectively. For all the devices, the optimum density of defects was kept at 1.0 × 1015 cm−2. It was established that the P3HT-based device can operate and be stable between 240–––340 K temperature window, while devices with CuSCN and Cu2O can work between 280 – 400 K temperature range. Thus, the CuSCN- and Cu2O-based devices were more stable because they could form a more compact structure that can withstand relatively higher operation temperatures than organic-based devices with P3HT as HTL. Finally, it was established that relatively cheaper metals such as Pt, Ni, and Pd can be suitable alternatives to expensive gold metal back contact. It is expected that this work would shed additional light on the utilisation of BaZrS3 as a promising absorber material in the actual manufacturing of solar cells for clean energy production.

Comprehensive Overview of GAN Technology: Architecture, Training, and Applications

Abstract: Generative Adversarial Networks (GANs) is a groundbreaking artificial intelligence technology that transforms generative modeling through the implementation of a novel adversarial training framework. GANs are made up of two neural networks, the generator and the discriminator, which compete in a minimax game in order to generate counterfeit samples of data and distinguish between real and fake data. This adversarial training method results in the creation of highly proficient generative models capable of producing data that is identical to real-world samples. GANs have been shown to have significant outcomes in a variety of programs, consisting of synthesizing images, style transfer, medical visualization, and natural language processing. However, GANs encounter difficulties with problems like mode collapse and operational irregularities. Presently, research is aimed at tackling these problems and strengthening GAN frameworks and training approaches. GANs’ adaptability and perspective contribute to an intriguing technology for a wide range of sectors and artistic activities, with consequences for artificial intelligence growth and generative modeling breakthroughs. GANs incorporate the potential of competitive training with their capacity to generate extremely realistic data in a variety of domains. We will begin by looking at the fundamental concepts, and underlying principles that reinforce GANs, and their general architecture, investigate their different possible uses, and discuss the obstacles and potential developments in this instantly transforming field.

An Overview of Text to Visual Generation Using GAN

Text-to-visual generation was once a cumbersome task until the advent of deep learning networks. With the introduction of deep learning, both images and videos can now be generated from textual descriptions. Deep learning networks have revolutionized various fields, including computer vision and natural language processing, with the emergence of Generative Adversarial Networks (GANs). GANs have played a significant role in advancing these domains. A GAN typically comprises multiple deep networks combined with other machine learning techniques. In the context of text-to-visual generation, GANs have enabled the synthesis of images and videos based on textual input. This work aims to explore different variations of GANs for image and video synthesis and propose a general architecture for text- to-visual generation using GANs. Additionally, this study delves into the challenges associated with this task and discusses ongoing research and future prospects. By leveraging the power of deep learning networks and GANs, the process of generating visual content from text has become more accessible and efficient. This work will contribute to the understanding and advancement of text-to-visual generation, paving the way for numerous applica-tions across various industries.

Generic Framework for Vehicle Identification System with Deep Learning Models

In contemporary vehicle scenario, different kinds of vehicles are playing vital role for the customers. Day by day, vehicles are increasing with different properties and identifications. Monitoring and maintain the records of vehicles in digital platform is the challenging task as well as a crucial task for the countries. Here, we have to take the scenario of digitalization of vehicles with deep learning network models for identification and maneuvering process. In our country, different states are following multiple vehicle identification like numbering, coloring and imaging. Many researchers were contributing towards this identification mechanism like supervised, unsupervised models for getting optimum accuracy. Text and Image classification are not sufficient to solve this vehicle identification problem. We are incorporating text, image and video related unstructured data set to solve this problem. We have framed a generic architecture for all kind of vehicles analyze the data. For instance, a vehicle is moving towards CCTV camera and it has been recorded as video content. In this paper, we come across video to still pictures and image segmentation and all the identification parameters like White, Yellow and green. We performed both text classification and image classification algorithms to acquire effective result compared to existing algorithms. Bi Directional RNN (LSTM/GRU) and LeNet, Alex Net, Inception, VggNet and ResNet algorithms are analyzed for this entire process. This is one of the globally challenging scenarios for identification with feature extraction, data cleaning and processing. At the end of the result, we achieved 98.94 % accuracy compare than other existing systems.

Piezoelectric Sensors as Energy Harvesters for Ultra Low-Power IoT Applications.

The aim of this paper is to discuss the usability of vibrations as energy sources, for the implementation of energy self-sufficient wireless sensing platforms within the Industrial Internet of Things (IIoT) framework. In this context, this paper proposes to equip vibrating assets like machinery with piezoelectric sensors, used to set up energy self-sufficient sensing platforms for hard-to-reach positions. Preliminary measurements as well as extended laboratory tests are proposed to understand the behavior of commercial piezoelectric sensors when employed as energy harvesters. First, a general architecture for a vibration-powered LoRaWAN-based sensor node is proposed. Final tests are then performed to identify an ideal trade-off between sensor sampling rates and energy availability. The target is to ensure continuous operation of the device while guaranteeing a charging trend of the storage component connected to the system. In this context, an Ultra-Low-Power Energy-Harvesting Integrated Circuit plays a crucial role by ensuring the correct regulation of the output with very high efficiency.

Optimizing industrial transport with a connected automated vehicle demonstrator for assembly systems and end-of-line production

In recent years, the automotive industry has witnessed significant progress in the development of automated driving technologies. The integration of advanced sensors and systems in vehicles has led to the emergence of various functionalities, such as driving assistance and autonomous driving. Applying these technologies on the assembly line can enhance the efficiency, safety, and speed of transportation, especially at end-of-line production. This work presents a connected automated vehicle (CAV) demonstrator for generating autonomous driving systems and services for the automotive industry. Our prototype electric vehicle is equipped with state-of-the-art sensors and systems for perception, localization, navigation, and control. We tested various algorithms and tools for transforming the vehicle into a self-driving platform, and the prototype was simulated and tested in an industrial environment as proof of concept for integration into assembly systems and end-of-line transport. Our results show the successful integration of self-driving vehicle platforms in the automotive industry, particularly in factory halls. We demonstrate the localization, navigation, and communication capabilities of our prototype in a demo area. This work anticipates a significant increase in efficiency and operating cost reduction in vehicle manufacturing, despite challenges such as current low traveling speeds and high equipment costs. Ongoing research aims to enhance safety for higher vehicle speeds, making it a more viable business case for manufacturers, considering the increasing standardization of automated driving equipment in cars. The main contribution of this paper lies in introducing the general concept architecture of the integration of automated driving functionalities in end-of-line assembly and production systems. Showing a case study of the effective development and implementation of such functionalities with a CAV demonstrator in a more standardized industrial operational design domain.

Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

A digital twin uses a virtual model of the physical system to fulfill the diverse requirements (e.g., latency, reliability, quality of physical experience) for emerging vehicular network applications. Although a twin-based implementation of vehicular networks can offer performance optimization, modeling a digital twin is a significantly challenging task. Federated learning (FL) is a better privacy-preserving, distributed learning scheme that can be used to model twin models. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for a twinbased vehicular network. A general architecture for FL-enabled digital twins for a vehicular network is presented. Our proposed architecture consists of two spaces, such as twin space and the physical space. A physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas, the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the paper is concluded and an outlook on open challenges is presented.

Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle.

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modeling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous noun phrases as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots.

Calibration of multiple shunted piezoelectric transducers with correction for residual modes and shunt interactions

Piezoelectric shunt damping can be used for efficient vibration mitigation of a single or multiple vibration modes of a structure. Several analytical tuning expressions have been derived for numerous circuit topologies for a single shunt. However, when multiple shunted piezoelectric transducers are attached to a flexible structure, their individual interactions alongside the spill-over from residual modes affect the calibration accuracy. In this paper, explicit correction terms that represent these effects are presented for multiple shunted piezoelectric transducers targeting a single vibration mode. The correction terms are obtained from the evaluation of effective electromechanical coupling factors, while proper balancing between shunts follows from an assumed proportionality between shunt forces. The proposed correction method is applicable to general shunt architectures, for which tuning is available when targeting a single-degree-of-freedom structure. Its ability to regain nearby optimal damping properties is illustrated numerically for the classic series RL shunt.

Lexiques et corpus au service de la littératie océanique : propriétés et relations lexicales dans le domaine de la faune marine

Lexicons and Corpora Serving Ocean Literacy: Properties and Lexical Relations in the Field of Marine Fauna. In this article, we present the first steps of the Ocean Literacy project, which aims to promote the creation and processing of a comparable medium-sized specialised corpus (French-Italian) in the field of marine fauna as well as the creation of a lexicographic resource to describe the lexical relations between the terminological units (TUs) of the field in question, using the theoretical framework of Explanatory and Combinatorial Lexicology. After describing the methodological and theoretical choices made when compiling and processing the ZooCor (fr-it) corpus, we will proceed to the analysis of the French term candidates resulting from semi-automatic extraction using TermoStat software. We will then describe the general architecture of the lexicographic resource ZooTerm which is currently under development, and explain, with the help of a terminographical data model and concrete examples, why we chose to use lexical functions to systematically encode the relations between terms associated with the marine fauna domain. Finally, we will discuss the results and the main theoretical issues raised from this exploratory work. Keywords: lexicon, corpus, ocean literacy, marine fauna, lexical relations

General Dynamics for Single- and Dual-Axis Rotating Rigid Spacecraft Components

Deriving and propagating a spacecraft’s equations of motion is fundamental to describing its behavior accurately. These equations of motion depend on the spacecraft’s configuration, which includes any physical subsystem such as attitude control devices, solar panels, gimbals, etc. Prior work introduced the backsubstitution method to yield a modular and scalable formulation to develop complex spacecraft dynamics specific to rotating components attached to a rigid hub as effectors. This paper relaxes assumptions made in deriving effector components in prior work, such as mass properties and frame definitions. This produces a general architecture that uses common equations of motion for physically equal parts. The result is an analytical solution of a set of general rotating effector equations of motion that greatly expand the configuration space of spacecraft that can be simulated with the backsubstitution method. In contrast to prior work where the rotations are highly constrained, rigid-body components can rotate about one or two general axes, and the component mass distribution can be general, no longer requiring the component’s principal axis to align with the center of mass or hinge axis. A numerical software solution demonstrates and verifies how these effectors can mimic a range of dynamic spacecraft components.

3D modeling of the stratigraphic and structural architecture of the Crotone basin (southern Italy) using machine learning with Python

The 3D modeling and representation of geological data have experienced significant growth within last years, due to the use of new technologies derived from advancements in land representation methods. These technologies enable interactive, intuitive and clear geological visualizations. This paper shows how, by using the open-source Python software (operable with a simple internet browser) for machine learning (linear and KNN interpolations), together with Geographic Information Systems (GIS), it is possible to achieve interactive 3D visualizations of geological features in sedimentary basins. This study is performed in the onshore-offshore Crotone area (southern Italy) where a large amount of stratigraphic datasets are available from core perforation and seismic profiles due to the presence of a natural gas extraction field. Thanks to a database of 63 drilling lithologic records and 43 check points obtained from 9 interpreted seismic sections, several 3D HTML models were constructed defining three stratigraphic units (Pre-Messinian, Messinian, and Post-Messinian). An overlap of the Post-Messinian top surface and an erosional truncation of the Messinian top surface toward the N were observed, together with a rising of the Pre-Messinian top surface in the northwestern area. This stratigraphic architecture may indicate differential subsidence and/or uplifting due to syn-sedimentary fault kinematics in the whole studied area. The 3D models with the stratigraphic unit boundary surfaces obtained with KNN interpolation (showing stepped and abrupt edges) allowed the interpretation in terms of structural architecture and syn-sedimentary fault kinematics. Three main sets of faults were deduced: N–S; NNW-SSE, and ENE-WSW. A minorly represented E-W set was added to the main sets. These faults generated a horsts-grabens structure, and in many cases a determinate set of faults caused a progressive lowering or rising of some areas with an “en echelon” arrangement. According to previous works, these deduced sets of faults (most of them strike-slip faults) have a good agreement with the general structural architecture and defined faults in the area.

Unfolding of the neutron spectrum in a nuclear reactor using genetic algorithms method

The objective of this paper is to present the results of the neutron spectrum unfolding using a high-performance code that we have developed, which is based on genetic algorithm. This paper presents the methodology used for neutron spectrum unfolding and describes the general architecture of our developed code. It also includes results of neutron spectrum unfolding in the case of the ARC-700 reactor using our genetic algorithm-based code and using an iterative method. In order to evaluate and validate our code, a comparison between reference values and results obtained using the iterative and genetic algorithm methods was presented. The comparison is performed both at the spectra as well as at the reproduced activation scale. This comparison shows a very good agreement between the results obtained using our code and the reference values and also shows a clear distinction of the genetic algorithm method compared with the iterative method. This validation shows that our code can be successfully used for neutron spectra unfolding in nuclear reactors using the activation detectors approach.

Naturally segregating genetic variants contribute to thermal tolerance in a Drosophila melanogaster model system.

Thermal tolerance is a fundamental physiological complex trait for survival in many species. For example, everyday tasks such as foraging, finding a mate, and avoiding predation are highly dependent on how well an organism can tolerate extreme temperatures. Understanding the general architecture of the natural variants within the genes that control this trait is of high importance if we want to better comprehend thermal physiology. Here, we take a multipronged approach to further dissect the genetic architecture that controls thermal tolerance in natural populations using the Drosophila Synthetic Population Resource as a model system. First, we used quantitative genetics and Quantitative Trait Loci mapping to identify major effect regions within the genome that influences thermal tolerance, then integrated RNA-sequencing to identify differences in gene expression, and lastly, we used the RNAi system to (1) alter tissue-specific gene expression and (2) functionally validate our findings. This powerful integration of approaches not only allows for the identification of the genetic basis of thermal tolerance but also the physiology of thermal tolerance in a natural population, which ultimately elucidates thermal tolerance through a fitness-associated lens.