Classical Path Research Articles

Examination of the Impact of System Quality, Information Quality, and Service Quality on Net Benefits through User Satisfaction in the Performance of State Civil Apparatus in the Malang District Government

Information technology investment is currently occurring rapidly and has become a necessity in an organization, both in the business environment and in the government environment. The various characteristics of information systems created with all their advantages and disadvantages have become an arena for competition in terms of innovation to show the superiority of the organization. The Civil Servant Performance Information System is the management and procedures for collecting, processing, analyzing, presenting, utilizing, and documenting civil servant performance data in an integrated manner. The net benefits of an information system are influenced by system quality, information quality, service quality, and user satisfaction. This research aims to analyze the influence of system quality, information quality, and service quality on net benefits through user satisfaction in the Malang Regency Government. This research is quantitative research with a saturated sampling method, so a sample of 72 respondents was obtained. The data analysis techniques used are descriptive analysis, classical assumption test, t-test, and path analysis. The research results concluded that system quality, information quality, and service quality have a significant effect on user satisfaction with civil servant performance information systems. User satisfaction also has a significant effect on the emergence of net benefits from the performance information system.

An Investigation of Classical Model Predictive Controller Path Tracking Performance of a Two-Wheel and Four-Wheel Steering Vehicle

Studies on self-driving vehicles have become a trend in recent years, and many systems have been developed to enable autonomous manoeuvre. Various methods have been used to improve path-tracking algorithms which increase vehicle performance, including tracking accuracy and stability. Path tracking is one of the primary problems for autonomous vehicles where the vehicle deviates from target paths, which leads to unnecessary counter-correction. Conventional front wheel steering is unable to satisfy the manoeuvre with a high lateral acceleration since the front steering angle is limited in accurately responding to vehicle dynamics. Moreover, the characteristics of front wheel steering vehicles affect handling stability due to the fact that the turning radius is larger than the vehicle itself. This disadvantageous can compromise safety during under-steer and over-steer situations. The main objective of this preliminary study is to investigate the performance of a four-wheel steering system (4WS) in path tracking for autonomous vehicles using a classical model predictive controller (MPC). Conventional two-wheel steering (2WS) tracking performance following the desired driving system with the same MPC controller is compared with 4WS vehicles. The driving system is developed using Driving Scenario Designer to extract the desired yaw angle and lateral position for controller references constructed in Matlab Simulink. Fixed MPC constraint, prediction, control horizon, yaw angle and lateral position weights were used to compare the performance between 2WS and 4WS vehicles. The simulation results show that 4WS is three times better than 2WS vehicles in tracking predetermined paths. 4WS vehicle show 74.55% better performance in lateral position tracking and 68.75% better performance in trailing predetermined yaw angle value. The simulation data from the preliminary study will be used as a guideline to develop an advanced controller of 4WS vehicles.

Discussion on the Automatic Design Method of Well Trajectory for Cluster Well Platform

Abstract Shale oil and gas and other unconventional oil and gas resources adopt the development mode of cluster well group to achieve economic benefit development. Cluster well groups have the characteristics of small spacing between wellheads and limited space, and generally adjust trajectory parameters by designing individual artificial Wells one by one to meet the needs of geological target hitting and anti-collision and circumnavigation. Trajectory design affects the whole body, and the change of trajectory of one well causes all Wells on the same platform to readjust design parameters, which seriously affects the design efficiency. Therefore, on the basis of summarizing the experience of manual trajectory design, combining classical path planning algorithm and global planning algorithm, an automatic trajectory design model is established. A* path optimization algorithm and ant colony algorithm are used to construct the routing strategy method and rule set, and adapt the efficient manual guidance strategy for different risk classifications to achieve efficient automatic routing. A cluster well trajectory automatic design and routing module has been developed. Automatic trajectory design tested 15 platforms, with an average time of 60s. The efficiency of trajectory design was greatly improved, and it has important guiding significance for the development of automated drilling technology.

On removing the classical-quantum boundary

We argue that it is adherence to the axiom of counterfactual definiteness and not to that of locality and realism that results in Bell inequality violations. Furthermore, this axiom is not supported in classical mechanics because of deeper implications that arise from the principle of stationary action. This means that the Bell inequality fails classically, effectively removing the classical-quantum boundary–a conclusion prophesized by Bell himself. An implication here is that a local hidden variable theory, in the configuration space of classical mechanics, may not be ruled out. Basing our idea on Jacobi’s “initial variable” framework, we propose that a classical path of stationary action that recedes into the infinite past and stretches into an infinite future implies a reality that lacks counterfactual definiteness. We then corroborate this with recent experimental results, through which it could be understood that our world could be such a reality.

Mediating Effects of Earnings after Tax: Cost of Good Sold, Sales Growth, and Dividend Payout Ratio

The research aims to analyze the mediating effect of Earning After Tax on the influence of Cost of Good Sold (COGS) and Sales Growth on the Dividend Payout Ratio (DPR). This research uses a descriptive verification method with a quantitative approach. The research method uses classical assumption, associative, and path analyses. The analysis in this research is supported by the use of SPSS for Windows version 23. The data is secondary data from the Indonesian Stock Exchange website (www.idx.co.id). Based on the research results, it was concluded that there is a significant influence between the Cost of Good Sold (COGS) on the Dividend Payout Ratio (DPR) in consumer goods industry companies listed on the Jakarta Islamic Index (JII) for the 2011-2019 period with the results t count > t table (2.075 > 2.042) so Ha is accepted. There is an insignificant influence between Sales Growth on the Dividend Payout Ratio (DPR) in consumer good industry companies listed on the Jakarta Islamic Index (JII) for the 2011-2019 period because t count < t table (1.458 < 2.042), so Ho is accepted. There is a significant influence between Earning After Tax (EAT) and the Dividend Payout Ratio (DPR) in consumer goods industry companies listed on the Jakarta Islamic Index (JII) for the 2011-2019 period. Ha is accepted with the results t count > t table (4.237 > 2.042). Meanwhile, the Cost of Good Sold (COGS) influences the Dividend Payout Ratio (DPR) through Earning After Tax (EAT) Z value (2.702) and p-value 0.006 < 0.050. Moreover, Sales Growth influences the Dividend Payout Ratio (DPR) through Earning After Tax (EAT) Z value (2.178) and p-value 0.0296 < 0.050.

Curriculum Design and Sim2Real Transfer for Reinforcement Learning in Robotic Dual-Arm Assembly

Robotic systems are crucial in modern manufacturing. Complex assembly tasks require the collaboration of multiple robots. Their orchestration is challenging due to tight tolerances and precision requirements. In this work, we set up two Franka Panda robots performing a peg-in-hole insertion task of 1 mm clearance. We structure the control system hierarchically, planning the robots’ feedback-based trajectories with a central policy trained with reinforcement learning. These trajectories are executed by a low-level impedance controller on each robot. To enhance training convergence, we use reverse curriculum learning, novel for such a two-armed control task, iteratively structured with a minimum requirements and fine-tuning phase. We incorporate domain randomization, varying initial joint configurations of the task for generalization of the applicability. After training, we test the system in a simulation, discovering the impact of curriculum parameters on the emerging process time and its variance. Finally, we transfer the trained model to the real-world, resulting in a small decrease in task duration. Comparing our approach to classical path planning and control shows a decrease in process time, but higher robustness towards calibration errors.

Numerical Investigation of the Effects of Air Flow Geometry and Reynolds Number in Cooling Systems of Lithium-Ion Batteries

In this study, two factors affecting the cooling performance of lithium-ion batteries have been selected and investigated using numerical methods. They have been determined as airflow geometry design and different Reynolds numbers. First, the differences between a classical flow path design (Z channel) with air inlets and outlets in the same direction and an alternative design (U channel) with air inlets and outlets in different directions have been evaluated. To understand the effect of the turbulent properties of the fluid in both designs, the results of the analyses performed using different Reynolds numbers (Re=4000, 6000, 8000, 10000, 15000) have been compared with each other. As a result, it has been observed that the U-channel design provided more homogeneous cooling on the battery surfaces and that the heat transfer is at higher values. In addition, it was observed that the Nusselt number increased with the increase in the Reynolds numbers used in the analyses. This study provides important data to understand the effects of airflow geometry and Reynolds number on the design of lithium-ion battery cooling systems and contributes to the development of optimized cooling solutions.

Pengaruh Literasi Keuangan Terhadap Kinerja Keuangan Usaha Mikro Kecil dan Menengah (UMKM) dengan Perilaku Pengelolaan Keuangan sebagai Variabel Intervening

This study aimed to determine the influence of financial literacy on the financial performance of MSMEs with financial management behavior as an intervening variable. The method used in this research was quantitative. The population in this study consisted of MSME actors in the East Alok District, totaling 1,548 MSME actors. The sampling technique employed simple random sampling with a total sample of 94 respondents. The type of data used in this study was primary data. The data collection procedure utilized questionnaires measured with a Likert scale. The data analysis technique used in this study was a simple linear analysis method, including descriptive statistical tests, data quality tests, classical assumption tests, hypothesis tests, and path analysis. The results of the study indicated that: (1) Financial literacy did not affect the financial performance of MSMEs, (2) Financial literacy affected the financial management behavior of MSMEs, (3) Financial management behavior did not affect the financial performance of MSMEs, and (4) Financial management behavior could not mediate the influence of financial literacy on the financial performance of MSMEs.

Peran Kepuasan dalam Memediasi Hubungan Kualitas Pelayanan dan Harga pada Loyalitas Pelanggan di E-Commerce Shopee

The development of marketing today is no longer limited to time and space. This condition is caused by technological advances that are very supportive of conducting marketing activities in a wider scope. Both for service and non-service offerings. Along with the growth of demand by consumers through online, market place platforms have emerged that offer various consumer needs. So there is sharp competition among sellers. These conditions require companies to pay attention to the satisfaction felt by consumers which will ultimately create loyal consumers. The company should also consider the different elements that influence it. The focus of this research is to analyze how customer satisfaction mediates the relationship between service quality and price on loyalty. The sample involved was 80 Shopee e-commerce customers. Data testing includes, data quality test, classic assumption test, outlier test, and path analysis. A significant discovery is that satisfaction does not serve as a mediator in the relationship between service quality, price, and customer loyalty.

UAV data link anti-interference via SLHS-SVM-AdaBoost algorithm: Classification prediction and route planning

The ability to predict the anti-interference communications performance of unmanned aerial vehicle (UAV) data links is critical for intelligent route planning of UAVs in real combat scenarios. Previous research in this area has encountered several limitations: Classifiers exhibit low training efficiency, their precision is notably reduced when dealing with imbalanced samples, and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary. This paper proposes the sequential Latin hypercube sampling (SLHS)-support vector machine (SVM)-AdaBoost algorithm, which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization. Additionally, it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm. Through comparison, the modeling efficiency, prediction accuracy on the test set, and macro-averaged values of Precision, Recall, and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%, 5.7%, 36.0%, 25.0%, and 34.2%, respectively, compared with Grid-SVM. Additionally, these values are improved by 22.2%, 2.1%, 11.3%, 2.8%, and 7.4%, respectively, compared with particle swarm optimization (PSO)-SVM-AdaBoost. Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm, the classification prediction model of anti-interference performance of UAV data links, which took factors like three-dimensional space position of UAV and antenna bearing into consideration, is established and used to assess the safety of the classical flying path and optimize the flying route. It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path, and that intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered while reducing the route length by at least 2.3%, thus benefiting both safety and operation efficiency.

Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning

Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS2. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds.

Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.

All-body concept and quantified limits of cooperativity and related effects in homodromic cyclic water clusters from a molecular-wide and electron density-based approach.

We strongly advocate distinguishing cooperativity from cooperativity-induced effects. From the MOWeD-based approach, the origin of all-body cooperativity is synonymous with physics- and quantum-based processes of electron (e) delocalization throughout water clusters. To this effect, over 10 atom-pairs contribute to the total e-density at a BCP(H,O) between water molecules in a tetramer. Intermolecular all-body e-delocalization, that is, cooperativity, is an energy-minimizing process that fully explains non-additive increase in stability of a water molecule in clusters with an increase in their size. A non-linear change in cooperativity and cooperativity-induced effects, such as (i) structural (e.g., a change in d(O,O)) or topological intra- and intermolecular properties in water clusters (e.g., electron density or potential energy density at bond critical points) is theoretically reproduced by the proposed expression. It predicted the limiting value of delocalized electrons by a H2O molecule in homodromic cyclic clusters to be 1.58e. O-atoms provide the vast majority of electrons that "travel throughout a cluster predominantly on a privileged exchange quantum density highway" (⋅⋅⋅O-H⋅⋅⋅O-H⋅⋅⋅O-H⋅⋅⋅) using Bader's classical bond paths as density bridges linking water molecules. There are, however, additional electron exchange channels that are not seen on molecular graphs as bond paths. A 3D visual representation of the "privileged" and "additional" exchange channels as well as detailed intra- and inter-molecular patterns of e-sharing and (de)localizing is presented. The energy stabilizing contribution made by three O-atoms of neighboring water molecules was found to be large (-597 kcal/mol in cyclic hexamer) and 5 times more significant than that of a classical O-H⋅⋅⋅O intermolecular H-bond.

Pengaruh Lingkungan Kerja terhadap Loyalitas Karyawan dengan Kepuasan Kerja sebagai Variabel Mediasi di The Axana Hotel Padang

The purpose of this study was to determine: (1) the influence of the work environment on job satisfaction; (2) the influence of job satisfaction on employee loyalty; (3) the influence of the work environment on employee loyalty; (4) the influence of the work environment on employee loyalty with job satisfaction as an intervening variable. This study is a quantitative study. The population in this study were employees of The Axana Hotel. The research sample was 89 respondents. Data collection was carried out using a distributed questionnaire method. The data analysis technique used was regression analysis supported by the classical assumption test, F test, and path analysis test, with the help of the SPSS version 25.0 program. The results of the data analysis showed that the work environment had a positive and significant effect on job satisfaction with a Beta value or Standardized Coefficient of 0.261 with t count (2.523) with a significance of 0.013 <0.05. Job satisfaction has a positive and significant effect on employee loyalty with a Beta or Standardized Coefficient value of 0.338 with a t count (3.346) with a significance of 0.001 <0.05. The work environment has a positive and significant effect on employee loyalty with a Beta or Standardized Coefficient value of 0.605 with a t count (7.087) with a significance of 0.000 <0.05. And the work environment has a positive and significant effect on employee loyalty with satisfaction as a mediating variable at TheAxana Hotel. This can be seen in the results of the path analysis which produces a total influence value greater than the direct influence (0.693> 0.088).

The Role Of Brand Awareness Mediates The Influence Of Social Media Marketing On The Purchase Intention Of Spotify Premium Services

Purchase intention is a form of consumer behavior that wishes to own a product based on the experience, use, and attractiveness of a product. Purchase intention arises because of stimuli that come from within consumers and provide encouragement or motivation to own a product. This study aims to examine the role of brand awareness in mediating the effect of social media marketing on the purchase intention of Spotify premium services for people in Denpasar City. The population of this study is people in Denpasar City who have never purchased Spotify premium services, the number of which is not known with certainty (infinite). The sample was determined using a non-probability sampling method with a purposive sampling technique. Data collection was carried out by distributing questionnaires to 108 respondents. The analysis techniques used are descriptive statistical analysis, classical assumption test, path analysis, Sobel test, and VAF test. The results showed that social media marketing has a positive and significant effect on purchase intention. Social media marketing has a positive and significant effect on brand awareness. Brand awareness has a positive and significant effect on purchase intention. Brand awareness is able to partially mediate and increase the total influence of social media marketing on purchase intention. The results of this study provide implications for marketing strategies, especially in company management can develop marketing strategies by paying attention to social media marketing indicators and brand awareness that have good performance in this study in order to increase the purchase intention of Spotify premium services.

The Influence of Green Products on Green Repurchase Intention Through Green Advertising

Starbucks as a coffee shop retail company implements green products by launching several initiatives to reduce the environmental impact by using tumblrs and straws made of paper as well as carrying out green advertising.to increase public awareness of caring for environmental sustainability. This research aims to analyze the influence of green products on green repurchase intention through green advertising. The number of samples used was 100 people. The research method used is quantitative. The data analysis techniques used in this research are Validity Test, Reliability Test, Classical Assumption Test, Path Analysis, Sobel Test and VAF Test. From the research results it can be seen that green products have a positive and significant effect on repurchase intention, green products have a positive and significant effect on green advertising, green advertising has a positive and significant effect on repurchase intention and green advertising is able to mediate the influence of green products on repurchase intention. The results of this research can contribute to Starbucks Badung Regency by providing environmentally friendly information or logos on each product offered to consumers. In making advertisements, always add environmentally friendly elements, offering environmentally friendly products of good quality.

Balancing Flexibility and Stability as SDGs: Gig Economy Adoption, Flexible Recruitment Policies, and Employee Performance in North Sumatra’s Public Sector

Purpose: This study aims to develop a framework for understanding the impact of gig economy adoption and flexible recruitment policies on the performance of contractual government employees (PPPK) in the regional government of North Sumatra, with work engagement as a mediating variable. Design/methodology/approach: Data were collected from 320 PPPK employees through questionnaires. Statistical analyses, including classical assumption tests and path analysis, were conducted using SPSS to examine the direct and indirect effects of gig economy adoption and flexible recruitment policies on employee performance, mediated by work engagement. Findings: The results indicate that both gig economy adoption and flexible recruitment policies significantly and positively impact employee performance. Furthermore, work engagement serves as a significant mediating variable, enhancing the relationship between these independent variables and employee performance. This suggests that creating a balance between flexibility and stability in employment can improve PPPK performance. Research limitations/implications: The study focuses on PPPK employees in the North Sumatra regional government, including teachers, healthcare workers, and technical staff. Future research could expand the scope to include other types of employees and regions, as well as consider additional mediating variables such as job satisfaction and organizational support. Originality/value: Unlike previous studies that often address either gig economy or recruitment policies in isolation, this research integrates both elements and examines their combined effects on employee performance within a governmental context. The study provides a comprehensive framework for policymakers to enhance employee performance by balancing flexibility and stability in the workforce.

How Financial Performance Influence Stock Return? The Role of Earning Per Share

The purpose of this study was to determine the effect of CR, DER, and ROE on Stock Returns with EPS as an intervening variable in LQ45 companies for the period 2020-2023. Quantitative methods are applied in this study. The population used is companies that have been included in the LQ45 index for the period 2020-2023 so that the sample obtained is 208 sample data processed by purposive sampling technique. The data were analysis using descriptive analysis, classical assumption test, path analysis, and sobel test using SPSS 21 software. Research findings in equation 1 (EPS) show the results that CR has a negative effect on EPS, DER has no effect on EPS, and ROE has a positive effect on EPS. While in equation 2 (Stock Return) shows the results that CR, DER, and EPS have a negative effect on Stock Return and ROE has no effect on Stock Return. The results of path analysis explain that EPS can mediate CR on Stock Returns. Meanwhile, EPS cannot mediate DER and ROE on Stock Returns.

Pengaruh Program Pembinaan Mahasiswa Wirausaha (P2MW) Terhadap Minat Berwirausaha Melalui Locus Of Control Sebagai Variabel Intervening Pada Mahasiswa Universitas Negeri Surabaya

This study was conducted to determine the influence of entrepreneurial interest on Surabaya State University students after participating in the entrepreneurial student development program (P2MW) through locus of control as an intervening variable. This study used quantitative methods. The population in this study is Surabaya State University students who take part in the entrepreneurial student coaching program. The sample used was 90 respondents using saturated sampling techniques. The criteria for respondents to this study were students who participated in the entrepreneurial student development program organized by the Ministry of Education and Culture and Technology, the data analysis test techniques used were classical assumption tests, path analysis, and hypothesis tests processed using the IBM 25 version of the SPSS program. The results of this study did not have a significant effect on entrepreneurial student development interest. There is a significant influence of entrepreneurial student development programs on the locus of control. There is a significant influence of locus of control on entrepreneurial interest. The entrepreneurial student coaching program towards entrepreneurial interest through locus of control can mediate the variables of the entrepreneurial student coaching program and entrepreneurial interest. Keywords: Entrepreneurial Student Development Program, Locus of Control, Entrepreneurial Interest

Hubungan Kepemimpinan Pembelajaran Kepala Sekolah Lingkungan Kerja dan Iklim Sekolah Dengan Sikap Profesional Guru di SMP Negeri Kecamatan Pelaihari Kabupaten Tanah Laut

This research analyzes the direct and indirect relationship between Principal Learning Leadership, Work Environment, School Climate, and Professional Attitudes of Public Middle School Teachers in Pelaihari District, Tanah Laut Regency. This type of research is quantitative with an explanatory method using a path analysis model to analyze relationship patterns between variables. The research population was 140 teachers from 8 State Middle Schools in Pelaihari District, Tanah Laut Regency, and 104 respondents were taken as research samples using the Probability Simple Random Sampling technique. Data collection uses questionnaire instruments, and data analysis uses descriptive analysis, classical assumption testing, hypothesis testing, and path analysis. The results of the study of the regression coefficient values ​​for the Principal Learning Leadership and Teacher Professional Attitude variables were 0.423, the Work Environment and Teacher Professional Attitude variables were 0.279, the School Climate and Teacher Professional Attitude variables were 0.232, the Principal Leadership and School Climate variables were 0.430, the Work Environment variable and School Climate of 0.515, the Principal Learning Leadership variable through School Climate with Teachers' Professional Attitudes of 0.778 and the Work Environment variable through School Climate with Teachers' Professional Attitudes of 0.805. In conclusion, a partial direct relationship exists between Principal Learning Leadership, Work Environment, and Teacher Professional Attitudes in Public Middle Schools in Pelaihari District, Tanah Laut Regency. There is an indirect relationship between the Principal's Learning Leadership through School Climate and Teachers' Professional Attitudes and the Work Environment through School Climate and Teachers' Professional Attitudes in Public Middle Schools in Pelaihari District, Tanah Laut Regency. Keywords: Principal Learning Leadership, Work Environment, School Climate, Teacher Professional Attitude