Performance Evaluation Of System Research Articles

Performance Evaluation of Cocoyam-based Intercropping Systems to Different Rates of Fertilizers in Obio Akpa, South Eastern Nigeria

A field experiment was conducted at the Teaching and Research farm, Akwa Ibom State University Obio Akpa Campus during the 2021 and 2022 cropping seasons to assess the performance of cocoyam-based intercropping systems to different rates of organic and inorganic fertilizers. The experiment was a split-plot factorial laid out in a Randomized Complete Block Design with three replications. The treatments were six (6) rates of fertilizers (0kg/ha, NPK 200kg/ha, NPK 400kg/ha, Poultry Manure (PM) 2.5t/ha, PM 5.0t/ha, Mixture – NPK 200kg/ha + PM 2.5t/ha) and six (6) crop mixtures (Sole Cocoyam, Sole Melon, Sole Maize, Cocoyam + Maize, Cocoyam + Melon and Cocoyam + Melon + Maize) arranged in split plot. Result indicated significant differences (P< 0.05) among the various crop mixtures and fertilizer levels at 2, 4, 6 Months after planting (MAP) on the plant height, stem girth of cocoyam, plant height of maize and vine length of melon. PM 2.5t/ha favoured leaf Area, stem girth of cocoyam and maize with crop mixtures of cocoyam + maize, cocoyam + melon, cocoyam + melon + maize in both cropping seasons. NPK 200kg/ha and NPK400kg/ha gave higher values of cocoyam and maize height whereas PM 2.5t/ha recorded higher vine length of melon in sole cropping. PM 5.0t/ha gave higher values of corm yield, cormels yield, weight of cormels, number of cormels and length of cormels in sole cocoyam. For cocoyam + melon, higher values of corm yield, weight of corms, weight of cormels, number of cormels were obtained with PM 5.0t/ha. For cocoyam + maize, higher values of corm and cormel yields were obtained with Poultry Manure5.0t/ha and NPK400kg/ha respectively. The PM rate of 5.0 t/ha gave higher values of corm yield, weight of corm, weight of cormels and number of cormels with cocoyam + melon + maize in both seasons. For maize, PM 2.5t/ha gave higher values of grain yield with cocoyam + maize, cocoyam + melon + maize crop mixtures. For melon, PM 5.0t/ha and NPK 400kg/ha gave higher values with cocoyam + melon and cocoyam + Melon + maize respectively.

UPDs estimation and ambiguity resolution performance evaluation of LEO navigation system

Low-Earth orbit (LEO) satellites have fast motion and thus provide rapid geometric change relation to the ground stations in a short period of time, which are expected to improve the positioning performance. In this study, a LEO constellation of 160 LEO satellites was simulated and simulated LEO observation data at global, European, and American stations were used to estimate the uncalibrated phase delays (UPDs) and achieve static and kinematic precise point positioning (PPP) ambiguity resolution (AR). The quality of the estimated UPD products were evaluated. More than 89 % and 96 % of the wide- and narrow-lane UPDs had a posteriori residual of less than 0.15 and 0.25 cycles, respectively. In static PPP, all ambiguity fixing rates were larger than 63 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 8 %, 6 % and 7 %, respectively; the average root-mean-square errors (RMSEs) in the east, north, and up components at the global, European and American stations were decreased by (22 %, 20 %, 15 %), (36 %, 34 %, 28 %) and (44 %, 31 %, 29 %), respectively. In kinematic PPP, all ambiguity fixing rates were larger than 40 %. Compared with float solutions, the average convergence time of the fix solutions at the global, European and American stations was accelerated by 4 %, 19 % and 14 %, respectively; the average RMSEs in the east, north, and up components at the global, European and American stations were decreased by (10 %, 10 %, 7 %), (27 %, 22 %, 11 %) and (32 %, 20 %, 10 %), respectively. In both static and kinematic PPP, AR accelerated the convergence time and improved the positioning accuracy.

Development and performance evaluation of active insulation systems using solid-state thermal switches

Traditional building envelopes have passive insulation systems that cannot respond to dynamic changes in the environment. An Active Insulation System (AIS) consists of Active Insulation Materials (AIMs) that dynamically vary the thermal conductivity of the insulation system. Several researchers have evaluated the impact of AIS on building thermal and energy performance by using simulation tools. Up to 70% savings in annual heating and cooling energy and significant reductions in peak demand have been predicted for some climates with wall systems employing AIS. However, materials and assembly development still need a cost-effective product that achieves the required performance. In this study, we present the process of developing an AIS that we will install in a test hut for its performance evaluation. Minimum performance criteria of the AIS system are developed based on R-low/R-high ratio, required time and efficiency to switch states, and cost estimates. The following steps during this study are creating the concept to meet the requirements, predicting the performance via simulations, developing the experimental setup for bench-scale testing, and finally, constructing a full-scale wall assembly and monitoring the performance when exposed to environmental chamber tests. The selected approach uses off-the-shelf products to create an AIS that can switch R-value between 0.98 ft2·°F·h/BTU (0.173 m2·K/W) and 5.81 ft2·°F·h/BTU (1.02 m2·K/W) and have a switching time of less than one minute between R-high and R-low.

Performance evaluation of a nuclear facility monitoring system using multi-sensor network and artificial intelligence algorithm

As the use of nuclear and radiation technologies increases, the importance of radiation safety and monitoring increases. In this study, we develop a nuclear facility monitoring system (NFMS) for rapid response to radiation accidents in nuclear material storage facilities; (1) multi-sensor network based on NaI(Tl) detector and FPGA-DAQ system and (2) an artificial neural network (ANN) algorithm for tracking radiation source location. Energy resolution and sensitivity of the detectors were evaluated to accurately track the location of radioactive materials and to identify nuclides using the multi-sensor network. To evaluate the localization accuracy of NFMS, a test storage facility was built and experiments were performed. Localization accuracy was obtained by analyzing the measured counts for each detector using an artificial intelligence (AI) based ANN algorithm, confirming an accuracy of over 99 %. The developed NFMS is expected to contribute to the safe management of radioactive materials in nuclear facilities.

IMPLEMENTATION OF AN EFFECTIVE FSO WDM SYSTEM UNDER DIFFERENT ATMOSPHERIC CONDITIONS WITH DIFFERENT CODING SCHEMES

In different atmospheric conditions, Free Space Optical (FSO) transmission is vital because it sends data via light beams through the atmosphere. This technique transmits data quickly and efficiently with low signal attenuation in clear weather. This makes it ideal for short- to medium-range communication, especially in urban areas where cables or radio frequency spectrum are impractical. However, fog, rain, and snow can absorb or disperse the light signal, reducing transmission range and reliability for FSO. This research creates an effective FSO system that supports many channels and transmission distances. The suggested system will be tested in four weather conditions (light air, light rain, medium rain, and heavy rain) with attenuation values of 0.47, 1.988, 5.844, and 9.29 dB/km. Optisystem program version 21 designs and evaluates system performance based on QF, BER, and other criteria. In this research, multiple modulation formats are evaluated for best performance. QF exhibited a reversal relationship with distance, while BER showed a direct one. The suggested system can transmit for 25 km, 12 km, 10 km, and less than 6 km in light air, light rain, medium rain, and severe rain. This research also examines system performance under NRZ and RZ modulation formats. NRZ modulation is better for light air and clear weather transmission than RZ modulation since it requires less equipment and is easier to install. Since NRZ modulation requires no clock recovery overhead, bandwidth efficiency is usually higher. In light rain, RZ modulation minimizes optical fiber dispersion, extending transmission distances and improving signal quality. NRZ excels within 10 kilometers. NRZ works for 6 km and 4 km in medium and severe rain, although RZ's dispersion tolerance and synchronization make it better for longer transmission distances. For short distances, NRZ is suitable, but for longer distances, RZ is more resilient due to its better dispersion management and signal transmission.

A statistical model on heat transportation of hybrid magnetic nanoparticles with slip constraints on a heated shrinking sensor device using analysis of variance: Sensitivity analysis

A statistical model finds application across various engineering and scientific contexts, aiding in the analysis and enhancement of heat transfer processes. Its utility extends to supporting scientists and engineers in gaining deeper insights into the impact of diverse factors on heat transfer and optimizing procedures for enhanced efficiency. Notably, the heat transfer efficiency of hybrid nanofluids containing ferromagnetic nanoparticles outperforms that of conventional fluids. In this study, a statistical model is utilized to predict the increased heat transfer rate in a heated shrinking sensor employing hybrid magnetic nanoparticles with slip restrictions. Incorporating radiative heat flux and heat source/sink components contributes novelty to the study. The model is converted into a non-dimensional form using appropriate similarity rules, and the resulting problem is solved computationally using bvp5c, a built-in function in MATLAB. Using advanced mathematical modeling and simulations, this research evaluates system performance in various scenarios and identifies optimal conditions for maximizing heat transfer rates. The study's main outcomes are; that including the thermal radiation and Prandtl number enhances the heat transfer rate, whereas the heat source decreases it. Further, the increased modified Hartmann number and suction parameter raise the shear rate coefficient.

Enhancing employees’ quality of work life and engagement to foster corporate social responsibility: a data mining approach

PurposeThis paper aims to present a dynamic model for strategic and personalized decision-making in human resources (HR), using data mining techniques to enhance corporate social sustainability (CSS). The focus is on the interconnectedness of employee engagement (EE), enablement and the quality of work life.Design/methodology/approachThe proposed model integrates various HR data, including demographic information, job specifications, payment and rewards, attendance and absence, alongside employees’ perceptions of their work-life quality, engagement and enablement. Data mining processes are applied to generate meaningful insights for senior and middle managers.FindingsThe study implemented the model within a production organization, revealing that factors influencing EE and enablement differ based on gender, marital status and occupational group. Performance-based rewards play a significant role in enhancing engagement, regardless of the reward amount. Factors such as “being recognized for competency” influence engagement for women, while payment has a greater impact on men. Engagement does not directly influence the quality of work life, but subcomponents like perceived transparency and the organization’s processes, particularly the “employee performance evaluation system,” improve work-life quality.Research limitations/implicationsThe findings are specific to the studied organization, limiting generalizability. Future research should explore the model’s effectiveness in different cultural and organizational settings.Practical implicationsThe proposed model provides practical implications for organizations that enhance CSS. Organizations can gain insights into factors influencing EE and enablement by using data mining techniques, enabling informed decision-making and tailored human resource management practices.Social implicationsThis research addresses the societal concern regarding the impact of business activities on sustainability. Organizations can contribute to a more socially responsible and sustainable business environment by focusing on work-life quality and EE.Originality/valueThis paper offers a dynamic model using data mining and machine learning techniques for sustainable human resource management. It emphasizes the importance of customization to align practices with the unique needs of the workforce.

Cooperative Control and Performance Evaluation of a Linear MIMO Parabolic Spatiotemporal Dynamic System on Directed and Switching Topologies.

The current work concerns on cooperative control of exponential stabilization and control performance improvement in the spatial domain for a linear spatiotemporal dynamic system associated with multiple control actuators and multiple collaborative measurement sensors. By assuming that the system dynamics is modeled by a MIMO parabolic partial differential equation (PPDE) and each sensor can share measurement information with its topological neighbors in directed and switching topological networks, a cooperative control protocol is proposed to achieve the control aim of this article. With the help of a combination of multiagent consensus theory, Lyapunov's method, and integral inequality technique, sufficient conditions are presented for the closed-loop exponential stability of the PPDE in the norm |·|2 . Moreover, some performance indexes are defined to evaluate the closed-loop control performance improvement in the spatial domain. Extensive simulation results are finally presented for a simple numerical example and a practical heat treatment process to verify the effectiveness and performance improvement capability of the proposed cooperative control protocol.

Performance evaluation of Trombe wall systems in enhancing thermal comfort in residential buildings of Subtropical highland climate - A case of Hamirpur, Himachal Pradesh, India

Performance evaluation of Trombe wall systems in enhancing thermal comfort in residential buildings of Subtropical highland climate - A case of Hamirpur, Himachal Pradesh, India

Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis

Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis

System-level variation in evidence-based multidisciplinary cancer care delivery.

11064 Background: Widespread health system integration of hospitals continues to reshape the landscape of U.S. healthcare delivery. While complex cancer care has become centralized to high-volume centers, care for common cancers happens across the spectrum of hospital sizes, with known variation in receipt of evidence-based practice (EBP) based on system affiliation. Strategies are needed to evaluate system performance in order to identify system characteristics and processes that contribute to high-quality cancer care. Methods: Using a novel data linkage between the National Cancer Database and AHRQ Compendium of U.S. Health Systems, adult patients treated for breast, colon, melanoma, and thyroid cancers between 2010 and 2020 at Commission on Cancer (CoC) hospitals were identified. Systems were categorized based on number of CoC hospitals (small, 2-3; medium, 4-9; large, 10+). Eight EBP measures were developed using national multidisciplinary guidelines, and measure-specific cohorts were extracted to calculate system-level EBP performance and within-system variation using mixed effects multivariable logistic regression models. Systems were then ranked across all measures for performance and variation, and average rankings were compared by system size. Results: 3,052,453 patients with breast (60.0%), colon (17.0%), melanoma (14.0%), and thyroid (9.0%) cancers were treated at 866 CoC hospitals in 368 health systems. The 8-measure analytic cohort had 104 systems, most of which were small systems (n=74, 71.2%). Large systems (n=9, 8.7%) had worse average performance rankings compared to medium or small systems, though this was not statistically significant (p=0.1) (Table). A significant increase in within-system variation (as measured by average ranking of variability across EBP measures) was seen with increasing system size (p<0.001). Conclusions: Significant variation in EBP performance for common cancers was noted based on health system size in this large, contemporary cohort, with greater within-system variation as system size increased. Future work will identify modifiable system characteristics associated with performance that could aid system leaders and policy makers to design, measure, and incentivize systems that employ effective cancer care delivery strategies. System rankings from 1 to 104 (best to worst), averaged over all measures; data presented as median (IQR). [Table: see text]

Innovation and Practice of Students' Evaluation of Teaching

Students' evaluation of teaching is to establish a platform between students and teachers, which can make them fully trust, so as to convey the real feelings of students and teachers, and improve the quality of teaching. However, in the actual implementation process, we pay too much attention to the evaluation of teaching results, which leads to the failure of the evaluation mechanism, which also leads to the failure of students' evaluation system. However, this does not mean that the student evaluation system itself is not good, this paper will analyze the existing shortcomings of the domestic university rating mechanism, and then give the solution strategy. At present, the innovation and ideological and political teachers in higher vocational schools still lack an effective training and performance evaluation system, and also ignore the era of "post class competition and certification", as well as the demand for knowledge and innovation ability of university teachers. Teachers are ordinary people and have great laziness. Teachers are not omnipotent. The quality of education cannot meet the requirements only by consciously lacking the restriction of system and evaluation mechanism.

Comprehensive performance evaluation of basic type of lunar habitat

ABSTRACT Amid the burgeoning lunar economy, habitat establishment is vital for exploration. The investigation into lunar habitat morphology requires systematic organization. To systematically and comprehensively delve into lunar habitats morphological design, this study analyzes spatial elements and relationships through the profiling of lunar habitats. It comprehensively analyzes spatial elements and relationships, laying the groundwork for a dedicated performance evaluation system. It provides theoretical and technical references for lunar habitat forms in the early design phase. First, it summarizes four basic types of lunar habitat profiles. Second, it selects 23 factors influencing lunar habitat profile design, using the Delphi method to filter six major ones. Third, various simulation experiments assess each type’s performance under factors. Fourth, the TOPSIS method analyzes comprehensive performance. This study has developed a comprehensive performance evaluation system and an integrated experimental methodology for assessing the architectural profile morphology of lunar habitat structures. Employing the evaluation system, a comprehensive experimental analysis was conducted on four basic types in the current phase. This analysis yielded two design suggestions for profile elements and three optimization directions for profile design. The discussion offers insights for future evaluations, considering mission innovation and technological advancements.

A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG

Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG’s capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate. Approach. We have constructed a nine-command BCI that operates on high-frequency SSVEF (58–62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation. Main Results. The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both the Z and Y axes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min−1 with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min−1. Significance. This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.

Development and performance evaluation of a high solar contribution resorption-compression cascade heat pump for cold climates

Absorption-resorption heat pumps (ARHPs) have great potential in utilizing low-temperature solar heat for efficient winter heating, but are limited by their weak adaptability to cold climates relative to absorption heat pumps (AHPs) and vapor compression heat pumps (VCHPs). In this work, a resorption-compression cascade system consisting of ARHP and VCHP subsystems is proposed and investigated, with the goal of achieving efficient winter heating with improved solar contribution in cold climates. Thermodynamic models of the two subsystems and of the whole cascade system are developed for system feasibility verification and performance evaluations. Feasible operation temperature and pressure conditions are identified in terms of internal subsystem matching. Based on these operation conditions, the primary energy ratio (PER) – which is a key performance evaluation index – is investigated over a range of solar fractions (f). The results show that for cases without solar contribution, the PER can be generally >0.95 under feasible operating conditions. In addition, the primary energy saving ratio (PESR) and heating capacity lift ratio (εlift), as well as the heat source temperature (Th) and ambient temperature (Tamb) restriction, are all investigated and compared to those of other, competing heating systems. The results show that the proposed system can reduce the Th demand to 69 °C (with a minimum Tamb of −21 °C) and extend the operational Tamb to −31 °C (with a minimum Th of 90 °C), while importantly achieving PESR > 0.20 and εlift > 20 % under the above extreme conditions. Moreover, when integrating the system with solar heat with f > 43 %, the proposed solar-assisted system has a clear PER advantage over an equivalent VCHP system in the Tamb range from −31 °C to 10 °C, highlighting the possibility of achieving higher solar contribution in cold climates.

Analysis of The Performance Appraisal & Bonus Payments at Real Ispat

The goal of the research project "Analysis of The Performance Appraisal & Bonus Payments at Real Ispat" is to examine the complex workings of performance assessment systems and how they affect worker satisfaction, productivity, and motivation. Acknowledging the pivotal function that incentive payments and performance reviews play in molding organizational results, this study attempts to offer a thorough examination of these mechanisms in the context of Real Ispat, a prominent participant in the steel production industry. This study's primary goal is to examine how performance reviews affect workers' motivation and output. Performance reviews are meant to provide workers input on how they perform at work, point out their areas of strength and weakness, and establish goals for the future. This study investigates the relationship between these evaluations and workers' incentive to improve performance and total output. Through gathering information from workers at different levels, the study seeks to measure how much performance reviews impact employee engagement and productivity, which in turn boosts the effectiveness and production of the company. Finding out how employees feel about and are satisfied with the performance evaluation process is the second goal. The success of the system depends heavily on how fair, transparent, and successful the assessment process is by the workforce. This survey looks at how Real Ispat employees feel about the assessment process, whether they think it's fair and impartial, and if they're happy with how often these appraisals are done. Since these impressions have a direct influence on employee morale, engagement, and retention, it is important to understand them. Evaluating the efficacy of Real Ispat's present performance evaluation system is the third goal. This entails determining whether the system of appraisals successfully identifies top performers and appropriately represents the work performance of employees. The study also looks at the impact that appraisals have on career development and how they help define reasonable and attainable performance goals. It also looks for areas where the assessment system needs to be improved in order to better fit with employee expectations and company goals. The study uses a mixed-method approach, integrating quantitative and qualitative data-gathering approaches, to accomplish these goals. Likert scale surveys are used to collect quantifiable information about worker productivity, satisfaction, motivation, and views. To obtain qualitative insights into employees' experiences and ideas for improvement, focus groups, and in-depth interviews are held. It is anticipated that the study's conclusions will give Real Ispat's management insightful information that will help them improve their processes for incentive payments and performance reviews. The organization may improve employee motivation, contentment, and productivity by implementing focused interventions after determining the present strengths and weaknesses of these systems. This study also adds to the larger body of knowledge on performance reviews by presenting best practices that other companies may use and providing actual data from the manufacturing industry. Finally, the "Analysis of The Performance Appraisal & Bonus Payments at Real Ispat" hopes to establish a standard for other businesses looking to optimize their performance assessment procedures in addition to improving Real Ispat's internal operations. This research aims to promote a more driven, productive, and contented workforce—which will eventually drive corporate success and growth—through thorough analysis and practical recommendations. Submit Research Paper Submit Research Paper Publication Procedure Publication Ethics Call for Papers Publication Topics Guidelines Disclamiar Copyright Infragmentation FAQ Terms and Conditions Digital Object Identifier Editorial Board Health Care+ Download Manuscript Template Copyright Form Sample Certificates

A Study on the Development of Driver Behavior Simulation Dummy for the Performance Evaluation of Driver Monitoring System

Driver monitoring system (DMS) was mainly developed to prevent accident risks by analyzing facial movements related to drowsiness and carelessness in real time such as driver’s gaze, blink, and head angle through cameras and warning the driver. Recently, the scope has been expanded to monitor passengers, and it has been linked to safety functions such as neglecting children, empty seats, or controlling airbags on seats with people under safety weight. However, evaluation research for algorithm advancement and performance optimization is relatively insufficient. In addition, the verification system is facing limitations such as personal information protection problems caused by the subject’s face data, errors in reproducing the subject’s drowsy and careless behavior, and differences in behavior according to individual differences. Therefore, as the importance of traffic safety is emphasized, an evaluation tool that can more efficiently and systematically evaluate the performance of DMS is needed. In this study, a driver behavior simulation dummy was developed that can quantitatively control the movement of the driver’s face and upper body. The driver behavior simulation dummy was developed in three stages in the order of function and specification definition, design and manufacture according to specifications, and verification through error tests for each function.

Design and fabrication of a novel microswitch made in LTCC-PDMS technology applicable to micro total analysis systems

AbstractThe physical design and implementation of an LTCC on–off microswitch device is demonstrated. One model was used to describe microchannel fluidic behavior, especially those with rectangular cross-section, routinely used in microfluidic devices. Another model to describe PDMS microbridge deformation was applied, allowing for estimating the elastic stiffness of several manufactured membranes. From this approach, microswitch design was conducted regarding the simulation results associated with both models, aiming at miniaturization. Applying LTCC-PDMS fabrication methodology, the fluidic devices were manufactured as established by theoretical design and an experimental setup was proposed in order to evaluate system performance. Experimental investigations indicate that a weight of 120 g was necessary to reduce the water flow rate of the normally open LTCC-PDMS microswitch from 0.42 mL/min to 50 μL/min when submitted to a controllable pressure-driven system. The leakage observed when testing the fabricated microswitches regards the rough way in which the PDMS membrane was attached to the LTCC body. However, experimental results suggest that the novel microswitch can operate satisfactorily regarding micro total analysis applications.

Experiment-Validated multiphysics modeling, generalizable deep learning and interpretable global sensitivity analyses for thermoelectric generators

Despite extensive studies on thermoelectric materials in a thermoelectric generator (TEG), the inverse design of TEG operating conditions remains challenging. Recent efforts in deep learning of TEGs using artificial neural network (ANN) lack global sensitivity analyses and interpretation of the connection-weights, leading to unknown coupling between input parameters and errors in importance ranking. This work reports a deep learning framework for TEGs using generalizable ANN, interpretable local (LSA) and global sensitivity analyses (GSA). Sensitivity analyses are applied to determine how energy conversion efficiency (η) under the practical Neumann and Robin thermal boundary conditions is affected by TEG operating conditions, including hot-side heat flux (qh), cold-side convective heat transfer coefficient (hf), temperature of the cooling medium (Ta), load resistance (RL), electric contact resistance (Rc), and thermal contact resistance (Kc). First, multiphysics TEG simulation results based on 3D finite element method (FEM) are validated by our experimental measurements using a self-built TEG performance evaluation system. The partial derivative-based LSA is done on a 1D theoretical TEG model to obtain the trend in sensitivity factors. Then, an ANN model is trained and validated based on the FEM simulation results and applied to the GSA process, aiming at reducing the computational load. Variance-based GSA is applied on the ANN model to determine the importance ranking and reflect the coupling between input parameters. The variance-based GSA ranks the importance as qh>hf>RL>Ta≈Rc≈Kc, and reflects the coupling between qh and hf. Finally, to interpret the ANN and the hidden deep learning process, a method combining the connection-weights algorithm and neural interpretation diagram (NID) is proposed to extract the hidden information in the connection weights. The ANN model is proven generalizable towards larger ranges of operating conditions. This work provides a framework to design TEG operating conditions towards higher energy conversion efficiencies.

OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT’s abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user’s intent, accurately extract system parameters from the user’s request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.