Performance Evaluations Research Articles

Strength Performance Evaluations of Vehicle Cylindrical LPG Tanks

This study aims to examine and compare the strength performances of vehicle cylindrical liquefied petroleum gas (LPG) tanks produced and used in Turkey, taking into account European and Turkish Standards. The LPG tanks were subjected to burst and fatigue tests to explore their burst pressures and fatigue performances using both experimental and computer aided techniques. To investigate the strength of the tanks, a universal test bench was developed and calibrated for use in both burst and fatigue tests. The obtained experimental results in terms of burst and fatigue failure locations for each brand of tank are compared with the results obtained using finite element based simulations. Visual solid models in 3D were drawn in SolidWorks and then ANSYS software was used to perform Finite Element Analysis (FEA) simulations on those LPG cylinders to obtain the results, such as stresses, deformations, burst and fatigue failure locations. As a result of this comparison, it has been observed that some brands of cylindrical LPG tanks are more durable and safe for use in vehicles. Since the same standard requirements and the same commercial material are used in LPG tank production, it is revealed that some companies need to reconsider their design, manufacturing and especially welding processes. The results of this independent and objective study can also be used as a warning for LPG tank manufacturers and as a guide for their customers in choosing a safe product.

Online functional connectivity analysis of large all-to-all networks in MNE Scan

Abstract The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity

Establishing Trust and Security in Decentralized Metaverse: A Web 3.0 Approach

The integration of blockchain and Web 3.0 technologies offers significant advancements in identity management and trust within decentralized Metaverse environments. Despite the rapid development of numerous Metaverse platforms, these environments often operate in isolation, lacking interoperability and cohesive security measures. This paper proposes a novel architecture leveraging Self-Sovereign Identity (SSI) principles and blockchain technology to achieve secure and interoperable interactions across different Metaverse platforms. Our framework encompasses a digital wallet application, an interactive virtual environment, and a secure blockchain-based backend infrastructure. We detail the system architecture, operational specifics, and demonstrate significant improvements in scalability, efficiency, and security through comprehensive performance evaluations. Additionally, we address the challenges of real-world implementation and propose viable solutions. By enhancing user control over personal data and enabling secure cross-platform interactions, our approach aims to foster a more secure, interoperable, and user-centric decentralized Metaverse.

Multi-variable optimization of a geothermal power plant integrated with water electrolyzer and methanol production

Renewable energy-based routes for methanol generation are recognized as essential strategies for eco-friendly energy conversion. Thus, the current study focuses on developing an innovative combined energy system for methanol production using direct carbon dioxide hydrogenation powered by geothermal energy. This is important because it addresses the need for sustainable and environmentally friendly methods of methanol production while also harnessing the potential of geothermal energy as a renewable resource. Therefore, in order to achieve multigeneration energy production, this work introduces and optimizes a novel configuration that benefits from an innovative heat design process. This configuration integrates an organic Rankine cycle, a single-flash geothermal power plant, an ammonia-water absorption chiller, a proton exchange membrane electrolyzer, and a methanol generation unit. Using the Aspen HYSYS tool, the multigeneration energy system is modeled and analyzed. As a result, the system is assessed using energy, exergetic, environmental, and economic evaluations. Furthermore, performance evaluations are carried out in the following modes: single generation, multigeneration, cooling, heat and power generation, and cooling. Furthermore, a genetic algorithm is employed to optimize the system in terms of economy and exergy. The energy and exergy efficiency are determined to be 32.57 % and 76.3 %, respectively, under the base case results. Furthermore, the rate of overall costs is 377.38 $/h. Furthermore, the precise output of carbon dioxide is −0.043 kg/kWh. The optimal unit cost and exergy efficiency for all items, taking into account the optimization scenario, are 3.34/GJ and 78.35 %, respectively. In addition, the optimum carbon dioxide and hydrogen rates for methanol generation are 92.49 mol/h and 200.51 mol/h, respectively.

Hardware Development and Evaluation of Multihop Cluster-Based Agricultural IoT Based on Bluetooth Low-Energy and LoRa Communication Technologies.

In this paper, we present the development and evaluation of a contextually relevant, cost-effective, multihop cluster-based agricultural Internet of Things (MCA-IoT) network. This network utilizes commercial off-the-shelf (COTS) Bluetooth Low-Energy (BLE) and LoRa communication technologies, along with the Raspberry Pi 3 Model B+ (RPi 3 B+), to address the challenges of climate change-induced global food insecurity in smart farming applications. Employing the lean engineering design approach, we initially implemented a centralized cluster-based agricultural IoT (CA-IoT) hardware testbed incorporating BLE, RPi 3 B+, STEMMA soil moisture sensors, UM25 m, and LoPy low-power Wi-Fi modules. This system was subsequently adapted and refined to assess the performance of the MCA-IoT network. This study offers a comprehensive reference on the novel, location-independent MCA-IoT technology, including detailed design and deployment insights for the agricultural IoT (Agri-IoT) community. The proposed solution demonstrated favorable performance in indoor and outdoor environments, particularly in water-stressed regions of Northern Ghana. Performance evaluations revealed that the MCA-IoT technology is easy to deploy and manage by users with limited expertise, is location-independent, robust, energy-efficient for battery operation, and scalable in terms of task and size, thereby providing a versatile range of measurements for future applications. Our results further demonstrated that the most effective approach to utilizing existing IoT-based communication technologies within a typical farming context in sub-Saharan Africa is to integrate them.

Estimating power in complex nonlinear structural equation modeling including moderation effects: The powerNLSEM R-package

The model-implied simulation-based power estimation (MSPE) approach is a new general method for power estimation (Irmer et al., 2024). MSPE was developed especially for power estimation of non-linear structural equation models (SEM), but it also can be applied to linear SEM and manifest models using the R package powerNLSEM. After first providing some information about MSPE and the new adaptive algorithm that automatically selects sample sizes for the best prediction of power using simulation, a tutorial on how to conduct the MSPE for quadratic and interaction SEM (QISEM) using the powerNLSEM package is provided. Power estimation is demonstrated for four methods, latent moderated structural equations (LMS), the unconstrained product indicator (UPI), a simple factor score regression (FSR), and a scale regression (SR) approach to QISEM. In two simulation studies, we highlight the performance of the MSPE for all four methods applied to two QISEM with varying complexity and reliability. Further, we justify the settings of the newly developed adaptive search algorithm via performance evaluations using simulation. Overall, the MSPE using the adaptive approach performs well in terms of bias and Type I error rates.

Phase modulation analysis on a cavity-structured single-unit thermoacoustic refrigerator

The establishment of traveling-wave-dominated acoustic fields within the regenerator is crucial for developing highly efficient thermoacoustic refrigeration systems. The present work introduces a novel looped, single-unit, direct-coupling thermoacoustic refrigerator, incorporating an optimally dimensioned cavity at a strategic location to maximize system performance. Central to this research is the systematic exploration of the phase modulation mechanism. This exploration involves adjusting the dimensions of the cavity structure, tracking its evolution from initial absence to full integration. The research method includes comprehensive evaluations of steady-state performance, onset characteristics, exergy loss, and axial parameter distribution analysis. The findings are visually depicted through simulations, highlighting the cavity's role in acoustic field modulation and its consequent impact on system efficiency. With the integration of an optimally sized and positioned cavity, the system's cooling efficiency improves by more than 2.5 times compared to the efficiency of a structure without cavities. A prototype replicating the proposed design was then constructed and experimentally tested. The results reveal a close correlation between the simulated and experimental data, affirming the analysis's accuracy and dependability. These studies effectively elucidate the significance of the cavity in looped single-unit thermoacoustic refrigerators and provide valuable insights for the development of more efficient phase modulation structures.

AUTOSAR-Compatible Level-4 Virtual ECU for the Verification of the Target Binary for Cloud-Native Development

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts.

A non-absorption-loss immune TDLAS sensor for online Mach number evaluation in supersonic flows

Tunable diode laser absorption spectroscopy (TDLAS) techniques are widely used in combustion monitoring by using direct absorption spectroscopy (DAS). However, the DAS method is sensitive to laser intensity fluctuations and fails to work in harsh application cases. A non-absorption-loss immune TDLAS sensor was proposed for robust online evaluation of Mach number from temperature and speed in supersonic flows and in situ validated on a scramjet. Spectral lines of H2O at 7185.58 and 7444.35 cm−1 were selected to perform evaluations of target molecules, i.e., of water vapor. In highly turbulent supersonic flows of Mach 2.8, the proposed TDLAS sensor has improved the signal-to-noise ratio (SNR) of the transmitted laser intensity by about 6.46 dB, compared to the conventional single-ended TDLAS sensor. When the ramjet was running stably, the average Mach number was 2.82, and standard deviations of the Mach number were less than 0.09. Relative errors of measured Mach numbers were less than 1.39 %. The proposed sensor monitors the entire ramjet engine process from ignition to propellant cut-off at a rate of 5 kHz and yields detailed insights for ramjet performance evaluation.

Charge storage mechanism and pseudocapacitance performance of NiO and Ce-doped NiO synthesized via modified combustion technique

Supercapacitors, as promising energy storage devices, have gained significant attention due to their ability to deliver high power and efficient charge storage mechanisms. In this work, we report the synthesis and electrochemical characterization of pristine NiO and Ni1-xCexO (x = 0.01, 0.02, 0.03) nanoparticles using a single-step auto ignition combustion method. Preliminary investigations indicate that Ni1-xCexO (x = 0.02) nanoparticles (2 % Ce doping) exhibit superior specific capacitance compared to other compositions. Therefore, we focused on structural, morphological, and vibrational studies on pristine and 2 % Ce-doped NiO. XPS and BET techniques were explored to assess the electronic state and porosity of the samples. Subsequent electrochemical performance evaluations included Cyclic Voltammetry (CV) at various scan rates, Galvanostatic Charge-Discharge (GCD), and Electrochemical Impedance Spectroscopic (EIS) techniques. From CV at a scan rate of 5 mVs−1, specific capacitance values of 419 Fg-1 and 604 Fg-1 were obtained for pristine and 2 % Ce-doped NiO, respectively. GCD studies revealed a specific capacitance of 381 Fg-1 for 2 % Ce-doped NiO at a current density of 1 Ag-1 and demonstrated capacitance retention of 95.1 % over 3000 GCD cycles for a current density of 4 Ag-1. The symmetric two-electrode supercapacitor cell demonstrates exceptional electrochemical performance, exhibiting a high specific capacitance of 122 Fg-1, an impressive energy density of 8.17 WhKg−1, a power density of 700 WKg-1 at 1 Ag-1, and a remarkable 88 % capacitance retention after 5000 GCD cycles. DFT calculations suggest that Ce doping significantly alters pristine NiO's electronic and structural features due to lattice strain. Our work highlights the enhanced electrochemical performance of 2 % Ce-doped NiO and its potential as a good-quality supercapacitor material.

Real-time privacy-preserved auditing for shared outsourced data

Health providers need to share patient information across healthcare networks efficiently and securely to improve medical and health services. Timely data synchronization among relevant parties is crucial for effectively containing and preventing the worsening of the condition. However, ensuring rapid information sharing while maintaining the security of sensitive patient data remains a pressing concern. In this paper, we introduce a cloud storage integrity auditing scheme that can protect auditors from procrastinating and preserve the privacy of sensitive information. Our proposed system requires healthcare institutions to encrypt sensitive patient data before uploading it to the cloud. It mandates the use of a data sanitizer for the secure processing of encrypted data blocks. Auditors must verify data integrity and promptly submit their audit results to the blockchain within a predefined time frame. Leveraging the time-sensitive nature of blockchain technology, healthcare institutions can monitor auditor compliance within the allotted validation timeframe. We conducted comprehensive security analysis and performance evaluations to demonstrate the feasibility and effectiveness of our solution in addressing the challenges of secure and timely cloud storage in healthcare settings.

A designated private set based trapdoor authentication scheme for privacy preserving trust management in decentralized systems

Authentication is crucial for network system security, relying on methods such as passwords, ID cards, biometrics, and behavioral characteristics. The conventional centralized authentication may lead to potential performance bottlenecks and privacy risks such as key exposure, single point of failure. Decentralized authentication systems using cryptographic techniques aim to address these issues but often tradeoff between flexibility and communication efficiency. In this paper we propose a new cryptographic concept called designated private set-based trapdoor authentication (DPSBTA) for flexible and efficient trust management in decentralized systems. DPSBTA eliminates the need for a trusted authority, with users’ access privileges defined by their private sets. During the authentication process, each server can designate an element set and only if a user holds adequate elements which are contained in the designated set can he obtains a credential from the server. The key features of DPSBTA include: decentralized trapdoor authentication management, without a trusted authority, conducted in a double threshold manner; privacy preservation, as servers do not know users’ element holdings or credential generation; round-optimal communication, with only two rounds of interaction between users and servers. We present the generic construction, security models, and concrete algorithms with correctness proof. The theoretical proof and the performance evaluations demonstrate the tangible security and high efficacy of the proposed DPSBTA.

Parallel Computing Techniques for Solving Large-Scale Partial Differential Equation

Two-layered partial differential equations (PDEs) will be structured in this investigation using a variety of parallel mathematical techniques, such as Red Dull Gauss Seidel and Gauss Seidel. For the solution of this study, the two-layered PDEs of the elliptic and illustrated sorts were selected. Parallel Virtual Machine (PVM) is used on the correspondence among all chips of the Parallel Handling System. PVM is well-known for its item system, which enables several diverse computers to be used as flexible and easy-to-use concurrent handling resources. An evaluation of the parallel exhibition assessment including the Gauss Seidel and Red Gauss Seidel techniques in terms of execution time, speed, capability, ampleness, and transient execution will follow the graphical presentation of the mathematical results. Since the goal of this work was to create a successful Two-Dimensional PDE Solver (TDPDES), performance evaluations are simple. Numerous design and mathematical sectors will see an improvement in their examination and investigation systems thanks to this new, effective TDPDES technology..

A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems

As the world braces for an era of ubiquitous and seamless connectivity, hybrid beamforming stands out as a beacon guiding the evolutionary path of wireless communication technologies. Several hybrid beamforming technologies are explored for millimeter-wave multiple-input multi-output (MIMO) communication. The aim is to provide a roadmap for hybrid beamforming that enhances wireless fidelity. In this systematic review, a detailed literature review of algorithms/techniques used in hybrid beamforming along with performance metrics, characteristics, limitations, as well as performance evaluations are provided to enable communication compatible with modern Wireless Intelligent Transport Systems (WITSs). Further, an in-depth analysis of the mmWave hybrid beamforming landscape is provided based on user, link, band, scattering, structure, duplex, carrier, network, applications, codebook, and reflecting intelligent surfaces to optimize system design and performance across diversified user scenarios. Furthermore, the current research trends for hybrid beamforming are provided to enable the development of advanced wireless communication systems with optimized performance and efficiency. Finally, challenges, solutions, and future research directions are provided so that this systematic review can serve as a touchstone for academics and industry professionals alike. The systematic review aims to equip researchers with a deep understanding of the current state of the art and thereby enable the development of next-generation communication in WITSs that are not only adept at coping with contemporary demands but are also future-proofed to assimilate upcoming trends and innovations.

Manager’s Trust and Trustworthiness in Sustainable Practices: Impact on Turnover and Manager’s Performance in Restaurants in China

This study examines the effects of managers’ trusting and trustworthy behaviors toward employees on turnover rates and performance evaluations within 115 restaurants in China. Utilizing data from a large-scale hypothetical experiment involving over 2000 employees and managers, combined with performance and operational data from these restaurants, we applied both game theoretical and empirical methods to analyze the dataset. Our findings reveal that managers’ trusting and trustworthy actions are significantly associated with lower turnover rates, highlighting how trust-driven leadership can enhance organizational stability and sustainable business practices. For senior associate managers, their trusting actions toward employees positively impact their performance. Furthermore, senior associate managers working in restaurants where employees are more trusting and trustworthy toward their peers tend to receive higher performance ratings. This study uniquely connects behavioral trust and trustworthiness measures with objective performance outcomes, offering new insights into the critical role of trust in fostering sustainable business practices and improving economic performance.

TAVA: Traceable anonymity-self-controllable V2X Authentication over dynamic multiple charging-service providers

The widespread deployment of Electric vehicles (EVs) leads to an increasing demand for charging piles and corresponding charging service (CS) from CS providers (CSPs). Pseudonym-based authentication mechanisms have been designed to resist the attacks which exploit the charging-authentication information to infer EV users’ identities and their driving routes. However, these existing mechanisms generated EV users' pseudonyms by relying on a trusted third entity, which affects the authentication system's resilience and EV user privacy-preservation.To this end, this paper proposes a Traceable Anonymity-self-controllable V2X Authentication (TAVA) scheme for the multiple-CSP (forming a CSP set) scenario, where each CSP independently manages its own CPs and a CSP randomly joins or leaves the CSP set. TAVA has a series of security capabilities. (1) First, it allows the mutual authentication between an EV user and a CP, while preserving EV user privacy and also assuring forward and backward security. This capability is achieved by using the multi-party computation technique to let all CSPs join the process of generating EV-users’ credentials but each CSP knows nothing about the credentials. (2) Secondly, TAVA has the capabilities of self-controllable anonymity and unlinkability by allowing each EV user to self-generate verifiable and unlinkable one-time pseudonyms based on bilinear- mapping technique. (3) At last, each EV user under TAVA is traceable. It is achieved by applying the two-factor authentication technique in TAVA and linking the one-time pseudonym to the two factors, namely, the credential and the EV user's biometric characteristics with low entropy rates. Note that all these security capabilities are achieved with less performance degradation in terms of communication and storage overheads in the dynamic environment. We conduct the informal and formal analysis of security capabilities and also make performance evaluations. The results indicate that, compared with the latest works, the computation overhead of the mutual authentication in TAVA is reduced by up to 89 %.

An Adaptive RF Front-End Architecture for Multi-Band SDR in Avionics.

This study introduces a reconfigurable and agile RF front-end (RFFE) architecture that significantly enhances the performance of software-defined radios (SDRs) by seamlessly adjusting to varying signal requirements, frequencies, and protocols. This flexibility greatly enhances spectrum utilization, signal integrity, and overall system efficiency-critical factors in aviation, where reliable communication, navigation, and surveillance systems are vital for safety. A versatile RF front-end is thus indispensable, enhancing connectivity and safety standards. We explore the integration of this flexible RF front-end in SDRs, focusing on the detailed design of essential components, such as receivers, transmitters, RF switches, combiners, and splitters, and their corresponding RF pathways. Comprehensive performance evaluations confirm the architecture's reliability and functionality, including an extensive analysis of receiver gain, linearity, and two-tone test results. These assessments validate the architecture's suitability for aviation radios and address considerations of size, weight, and power-cost (SWaP-C), demonstrating significant gains in operational efficiency and cost-effectiveness. The introduction of the new RF front-end on a single SDR board not only substantially reduces size and weight but also adds up to 18 dB gain to the received signal. It also allows for a high level of design flexibility, enabling seamless software transitions between different radios and the capacity to manage three times more radios with the same hardware, thereby significantly boosting the system's ability to handle multiple radio channels efficiently.

Deep learning for image-based detection of weeds from emergence to maturity in wheat fields

Effective weed control in wheat (Triticum aestivum L.) fields is crucial for optimizing production and ensuring food security in semi-arid regions. The implementation of deep learning for weed detection could enable precise weed management, leading to enhanced wheat yield, increased income for growers through targeted herbicide use, and a reduced environmental footprint associated with herbicide application. Charlock mustard (Sinapis arvensis L.) [CM], creeping thistle (Cirsium arvense (L.) Scop) [CT], and forking larkspur (Consolida regalis Gray) [FL]) are among the most invasive weed species in wheat fields. Effective management of these weeds is essential to ensure optimal wheat productivity in the semi-arid regions. To detect weeds, an unmanned arial vehicle was used to collect images and videos from 185 wheat fields in Tokat, Turkey. The images were collected from five phenological periods of each species including seedling, rosette, vegetative, flowering, and fruiting (in total 15 classification process). These stages are either important for timely weed control or essential for understanding weed pressure threshold, the potential for increasing weed seedbank increase, and identifying herbicide resistant weeds. A total of 145,792 objects were labeled in the images to create the dataset, divided into 80 % for training + validation and 20 % for testing. The dataset was used with the YOLOv5 (You Only Look Once) deep learning architecture. The YOLOv5 was the newest YOLO model during this study carried out. All neural networks provided by YOLOv5 (nano, small, medium, large, and xlarge) were trained and the Precision, Recall, F-1 Score, and AUC (Area under the Curve) performances of the neural networks were evaluated. The YOLOv5s (small) was the most precise neural network in detecting weeds regardless of the species. In contrast, nano was the least precise neural network in detecting each weed species. For CT, when small neural network was used, precision of detection ranged from 0.87 (fruit stage) to 0.96 (rosette and vegetative stages). For, forking larkspur, the best neural network was achieved with YOLOv5 small and the precision ranged from 0.45 (fruit stage) to 0.93 (vegetative stage). Using YOLOv5 small in Charlock mustard, the most precise detection occurred at seedling, rosette, and vegetative stages (with precision of 0.96) and the least precise detection was recorded at the fruit stage (0.81). Other performance evaluations (Recall, F-1 Score, and AUC) were in agreement with those of Precision. Our results indicated that by using YOLOv5 small neural network, all three weed species could be detected at all growth stages with the precision of 0.86 with the exception of fruit stage. Future research should (1) focus on assessing YOLOv5 small neural network with the newest versions of YOLO and also (2) evaluate this neural network for detecting weed species in wheat fields to allow for improved weed control and future recommendations.

Measuring Teacher Personality Competence: Era 5.0 Challenges for Teachers in Indonesian Archipelago

Teachers in archipelagic regions face challenges such as limited access, geographical isolation, and cultural diversity, particularly in the Society 5.0 era. This study aims to assess the feasibility of an instrument for measuring teacher personality competency in this context. Using a quantitative approach, the study was conducted in Maluku Province, with a multistage random sample of 752 teachers from 94 schools. The instrument employed a modified four-point Likert scale, and data analysis was conducted using the PLS-SEM method to evaluate the measurement and structural models. Key findings include: 1) Exploratory Factor Analysis (EFA) identified six dominant factors and 29 items to measure teacher personality competency; 2) The measurement model demonstrated feasibility, with construct validity confirmed through convergent validity (loading factors 0.70 and Average Variance Extracted 0.50) and discriminant validity (Fornell-Larcker criterion and Heterotrait-Monotrait Ratio 0.90). Reliability was supported by Cronbach’s Alpha, Rho_A, and composite reliability values all exceeding 0.70. These results indicate that the instrument is suitable for assessing teacher personality competency in island regions, helping address the challenges of the Society 5.0 era. The study has important implications for Indonesian education stakeholders, providing a validated tool to guide teacher performance evaluations.

Pengaruh Komunikasi terhadap Kinerja Karyawan pada Hotel Tentrem Yogyakarta

Communication plays a crucial role in influencing employee performance. Performance decline is often caused by communication issues, particularly in the hotel back office, such as the lack of effectiveness in information delivery and miscommunication among employees. This study aims to analyze the impact of communication on employee performance at Hotel Tentrem Yogyakarta. The data used includes quantitative data from employee performance evaluations in 2023, the number of employees, employee communication levels from 2019-2023, and qualitative data from interviews with human resources. The study population consists of 241 employees at Hotel Tentrem Yogyakarta. Data was collected using documentation and questionnaires distributed to respondents. Data analysis techniques include validity testing, reliability testing, normality testing, heteroskedasticity testing, simple linear regression, t-test, and coefficient of determination (R²). The study findings show that the calculated t value (38.728) is greater than the t table value (0.1264) with a significance level of 0.000, indicating a positive and significant effect of communication on employee performance at Hotel Tentrem Yogyakarta. The coefficient of determination value of 0.863 indicates that 86.3% of the impact on employee performance is due to communication, while 13.7% is influenced by other factors outside the scope of this study.