Time Tracking Research Articles

Living by the Clock II: The Diffusion of Clock Time in the Early Hellenistic Period

Summary By the mid-second century BC, and possibly even earlier, clock time was widely known: from Rome to Bactria, from rulers to men of modest means. This is striking, as Greco-Roman clock time was not fully conceptualized until 300 BC. In this article, I aim to explain its rapid diffusion, by examining which groups in society were the first to adopt clock time and how they communicated the concept and its related practices to others. I make use of Everett Rogers’ model of the diffusion of innovations, which identifies various adopter categories and their typical characteristics. I identify two groups of early adopters of clock time. Firstly, there is the cosmopolitan elite at the intellectual hubs Alexandria and Athens. Through the travels and written word of this mobile group, the idea of clocks as prestige monuments and the use of clock time for precisely scheduled parties rapidly gained traction in other urban centers. The more important early adopters were, however, Hellenistic soldiers, in particularly the Ptolemaic cleruchs. Armies already had a tradition of carefully keeping track of time. Because they relied on the direct participation of a considerable portion of the male population, armies had direct ways to communicate usage of this innovation to a wide section of the non-elite population. Via mercenaries, military practices could also easily spread from one Hellenistic army to another. Back at home, the soldiers took their temporal regimes to (among other places) the gymnasia, which played an important role in teaching the artificial concept of clock time to the next generations.

Observer-based fuzzy T–S control with an estimation error guarantee for MPPT of a photovoltaic battery charger in partial shade conditions

To improve the efficiency and performance of a photovoltaic system (PV) an observer-based fuzzy controller design methodology is provided in the study. The desired controller is achieved by employing a combination of linear matrix inequalities (LMIs). The system consists of a photovoltaic generator (PVG) connected to a boost converter. A battery is linked to the boost converter to stock additional energy for further use. A fuzzy controller based on a T–S fuzzy type observer that guarantees a predefined L2 performance is suggested to achieve maximum power point tracking (MPPT) even under changing weather conditions. An optimal trajectory should be tracked to ensure maximum power operation. For this aim, a specific reference fuzzy model is proposed to create the aimed trajectories. Using this method, the system dynamics are precisely reproduced over a large range of operations. The whole T–S fuzzy methodology, suggested in this paper, aims to ensure the most efficient energy recovery to recharge a battery under partially shaded conditions, resulting in high system efficiency. The proposed method is simulated with MATLAB/SIMULINK and the simulation results, with realistic reference trajectories, are driven while taking into account climate variations. The analysis of these simulations, along with a comparison with two other commonly used approaches, led to the conclusion that the suggested strategy succeeded in reducing the tracking time, as well as eliminating the oscillation that often occurs around maximum power point (MPP).

Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA’s performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed—approaching the maximum Power Point 10–15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm’s overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.

Refined Coseismic Slip and Afterslip Distributions of the 2021 Mw 6.1 Yangbi Earthquake Based on GNSS and InSAR Observations

On 21 May 2021, an Mw 6.1 earthquake occurred in Yangbi County, Dali Bai Autonomous Prefecture, Yunnan Province, with the epicenter located in an unmapped blind fault approximately 7 km west of the Weixi-Qiaohou fault (WQF) on the southeastern margin of the Qinghai–Tibetan Plateau. While numerous studies have been conducted to map the coseismic slip distribution by using the Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and seismic data as well as their combinations, the understanding of deformation characteristics during the postseismic stage remains limited, mostly due to the long revisiting time interval and large uncertainty of most SAR satellites. In this study, we refined coseismic slip and afterslip distributions with nonlinear inversions for both fault geometry and relaxation time. First, we determined the fault geometry and coseismic slip distribution of this earthquake by joint inversion for coseismic offsets in the line-of-sight (LOS) direction of both Sentinel-1A/B ascending and descending track images and GNSS data. Then, the descending track time series of Sentinel-1 were further fitted using nonlinear least squares to extract the coseismic and postseismic deformations. Finally, we obtained the refined coseismic slip and afterslip distributions and investigated the spatiotemporal evolution of fault slip by comparing the afterslip with aftershocks. The refined coseismic moment magnitude, which was of Mw 6.05, was smaller than Mw 6.1 or larger, which was inferred from our joint inversion and previous studies, indicating a significant reduction in early postseismic deformation. In contrast, the afterslip following the mainshock lasted for about six months and was equivalent to a moment release of an Mw 5.8 earthquake. These findings not only offer a novel approach to extracting postseismic deformation from noisy InSAR time series but also provide valuable insights into fault slip mechanisms associated with the Yangbi earthquake, enhancing our understanding of seismic processes.

Deep predictive data representation model control for photovoltaic maximum power point tracking under partial shading conditions

As deep learning progresses in recent years, deep neural networks (DNNs) have penetrated applications in various industries. The global maximum power point (GMPP) tracking issue exists in photovoltaic (PV) systems. This study proposes a deep predictive data representation model control (PDRC)-based PV GMPP tracking method. The PDRC approach establishes a data representation control model centered on deep prediction by extracting the data features such as the current, voltage, and duty cycle of PV systems controllers on a time series basis. The proposed PDRC method can receive signals of voltage and current, then output duty cycle control signals. Thereby PDRC method can control PV output power to track GMPP efficiently. Finally, the proposed PDRC approach is evaluated with the perturbation observation method (P&O), the improved particle swarm algorithm (PSO-P&O), and the modified cuckoo algorithm (CS-P&O) to compare the PV GMPP tracking performance. In the simulation experiments, the PDRC approach increases the average tracking efficiency over the comparison algorithms by at least 7.729 % and decreases the average tracking time by at least 0.0155 s under partial shading conditions (PSCs). In the physical simulation validation, the proposed PDRC approach possesses minimal power perturbation and increases the average tracking efficiency over the comparison algorithms by at least 0.407 %.

Adaptive Free‐Will Arbitrary Time Tracking of a Class of Uncertain SISO Nonlinear Systems

ABSTRACTThis paper investigates the free‐will arbitrary time (FWAT) tracking problem for a class of single‐input single‐output (SISO) strict‐feedback nonlinear systems without/with parametric uncertainties. First, we design an FWAT backstepping tracking controller for strict‐feedback nonlinear systems without uncertainties in a recursive manner. It is shown that the proposed virtual controllers and their derivatives are continuously differentiable for the backstepping design, and the tracking error converges to zero within the pre‐specified settling time independent of design parameters and initial conditions and remains at zero after the pre‐specified settling time. Second, we address the adaptive FWAT tracking problem of SISO strict‐feedback nonlinear systems with parametric uncertainties in the practical stability sense. The continuously differentiable and nonsingular time‐varying adjustment functions for practical FWAT tracking are developed to transform error variables for the recursive backstepping design. Then, the adaptive FWAT backstepping tracking design and stability analysis strategy are established to ensure practical FWAT convergence within the pre‐specified settling time independent of design parameters and initial conditions. The designed adaptive virtual and actual controllers are continuous for all . Furthermore, a practical adaptive prescribed‐time tracking scheme is provided using the proposed practical adaptive FWAT tracking scheme. Finally, the effectiveness of the theoretical approaches is confirmed through a simulation example and an experimental result.

A novel Beluga Whale Optimization for maximum power tracking in photovoltaic systems under shading and non-shading conditions

Partial shading poses a significant challenge for PV systems as it introduces multiple peaks in the panel output characteristics, making the power extraction process more complex. Efficiently locating the global maximum power from such a complex, non-linear curve is crucial for optimal system performance. Conventional MPPT techniques often fail to achieve this, resulting in reduced efficiency. Moreover, many metaheuristic algorithms struggle to balance tracking accuracy, convergence time, oscillations, and computational complexity. Therefore, this manuscript proposes a novel natural-inspired Beluga Whale Optimization (BWO) technique that effectively track the global maximum power without compromising its performance. This iteration-based algorithm integrates effective exploration through twin equations, incorporating the influence of both the best and neighboring position with levy flight function to enhance effective exploitation, and additional particle updation using the whale fall process ensures simultaneous diverse searching phenomenon in complex search spaces resulting in smooth and faster convergence. The algorithm was extensively examined under USC and five PSCs on a 250 W panel using MATLAB 2021a Simulink platform. The effectiveness of the technique was benchmarked against state-of-the-art MPPT techniques such as PO, CSO, PSO, and GWO. The rating-based approach confirms that BWO outperformed these techniques with an average MPPT efficiency of 99.98 %, a tracking time of 0.3195 s, transient state oscillations of 10.85 %, and steady-state oscillations of just 0.000453 % within 4–5 iterations, and other related aspects. Finally, the hardware demonstration using Solar Array Simulator (SAS) validated the performance of the proposed algorithm in the real-time environment, guaranteed the suitability towards practical applications.

Project Time Management and Performance of Kenya Towns Water Supply and Sanitation Programme at Central Rift Water Works Development Agency.

The general objective of the study was to examine the influence of Project time management on the performance of Kenya Towns Water Supply and Sanitation Projects at Central Rift Water Works Development Agency specifically the study. The study was anchored by pickle jar theory, theory of constraints, theory of effective project implementation and communication theory. A descriptive survey design was used in this research. The study targeted project managers, consultants, coordinators, engineers, contractors, community representatives, and representatives from the Ministry of Water and Irrigation in three projects under the Kenya Towns Water Supply and Sanitation Programme, carried out by the Central Rift Valley Water Works Development Agency. The counties involved are Narok and Nyandarua. A sample size of 83 respondents was used. The participants in the study were chosen using a simple random selection method. Data was collected using a questionnaire and interview schedule. The analysis was both qualitative and quantitative. Quantitative data collected was analysed using Statistical Package for Social Sciences version 25. The study analysed both descriptive and inferential statistics. Thematic analysis was used to analyse qualitative data collected from the interview schedule. The analysis revealed a Pearson correlation of (r=0.512, p<0.05). This indicated a moderate positive and significant relationship between project time planning and the performance of Kenya Towns Water Supply and Sanitation Projects. The also showed a Pearson correlation of (r= 0.553 p<0.005) which indicates a positive and significant relationship, suggesting that effective prioritization of time-related tasks plays a crucial role in enhancing project performance. Additionally, the correlation analysis for time tracking yielded a Pearson correlation of (r=0.390 p<0.005). This suggests a moderate positive and statistically significant relationship between time tracking and project performance. Lastly, the study revealed the impact of project team communication on performance, finding a Pearson correlation of (r= 0.672 p<0.005). This indicated a strong positive and significant relationship, suggesting that effective communication among project teams significantly enhances performance outcomes. The findings indicated that 45.1% of the variation in the performance of the project could be explained by the independent variables studied, while 54.9% of the variation is attributed to other factors not covered in this study. The study concluded that project time planning, time prioritization, time tracking, and project team communication had an effect performance of Kenya Towns Water Supply and Sanitation Projects at Central Rift Water Works. The study recommends implementing structured time planning methods, enhancing time tracking, and fostering effective communication within teams to improve project performance. Future research should explore stakeholder engagement and the role of emerging technologies in water project management

Challenges and recommendations for collecting and quantifying implementation costs in practice: a qualitative interview study

BackgroundThe cost of implementation is typically not accounted for in published economic evaluations, which determine the relative value for money of health innovations and are important for allocating scarce resources. Despite key papers outlining relevant implementation costs, they continue to be under reported in the literature and often not considered in practice. This study sought to understand and outline current practices for capturing the costs associated with implementation efforts, with examples from the digital health setting.MethodsA qualitative study of semi-structured interviews with purposefully sampled experts in implementation science, health economics and/or digital health was conducted. The interview guide was informed by a literature review and was pilot tested. Interviews were digitally recorded and transcribed. A hybrid inductive/deductive framework analysis was conducted using thematic analysis to elicit key concepts related to the research question.ResultsInterviews were conducted with sixteen participants with specialist expertise in implementation science (n = 8), health economics (n = 6), and/or digital health (n = 8). Five participants were experienced in more than one field. Four key themes were elicited from the data: difficulty identifying and collecting implementation cost data; variation in approaches for collecting implementation cost data; the value of implementation costs; and collaboration enables implementation costing. Broadly, while interviewees recognised implementation costs as important, only some costs were considered in practice likely due to the perceived ill-defined boundaries and inconsistencies in terminology. A variety of methods were used to collect and estimate implementation costs; the most frequent approach was staff time tracking. Multidisciplinary collaboration facilitated this process, but the burden of collecting the necessary data was also highlighted.ConclusionsIn current practice, standardised methods are not commonly used for data collection or estimation of implementation costs. Improved data collection through standardised practices may support greater transparency and confidence in implementation cost estimates. Although participants had industry exposure, most were also academic researchers and findings may not be representative of non-academic industry settings.

Active fault‐tolerant control of multi‐unmanned aerial vehicle system with time‐varying topology

AbstractThis paper studies an active fault‐tolerant control problem for multi‐UAV (unmanned aerial vehicle) system with time‐varying topology subject to actuator failures. A polytopic model is used to construct the time‐varying topological structure. Based on the quantitative estimation method of actuator faults' upper limit, an adaptive fault‐tolerant tracking control strategy is established by an active adjusting control parameters to compensate for unknown actuator faults, so as to obtain consensus tracking under time‐varying topology. Furthermore, the comparison simulations with the traditional control algorithm are given to demonstrate the effectiveness of the obtained results. The tracking control strategy in this paper has better fault tolerance performance with short tracking time and superior robustness.

Application of the Clinical Outcomes, Healthcare Resource Utilization, and Related Costs Model in Chronic Obstructive Pulmonary Disease Patients

Introduction: The change in prevalence and management of chronic obstructive pulmonary disease (COPD) led to changes in outcomes and costs. We aimed to assess current clinical outcomes, resource utilisation, and costs in COPD. Methods: Retrospective, observational study of a cohort of consecutive COPD patients who visited the emergency department (ED) of a large tertiary hospital in 2018. The study measured baseline characteristics, 30-day and 1-year mortality, readmission, re-ED visit rates, and costs using the Clinical Outcomes, HEalthcare REsource utilisatioN, and relaTed costs (COHERENT) model, validated for heart failure. This model, featuring a colour graphic system, tracks time spent in different clinical situations (home, ED, hospital), considering vital status, healthcare resource use, and related costs. Results: In 2018, 2,384 patients with a primary COPD diagnosis visited the ED. The average age was 76 years, with 40% women. Observed mortality rates were 7.6% in-hospital, 8.5% at 30 days, and 23.4% at 1 year. The readmission rates were 9.9% and 36.1%, respectively. The cohort’s 1-year cost was approximately EUR 14.6 million (USD 15.95 million), with a median cost per patient of EUR 3,298 (USD 3,603.96). Hospitalisation incurred the highest costs, with initial hospitalisation and readmissions accounting for 44.7% and 42.6% of expenditures, respectively. Conclusion: One-year mortality and readmission rates for patients with COPD visiting the ED remain high with a significant economic impact on the health system. This burden justifies special programs to improve their care.

Implementation of a low-cost current perturbation-based improved PO MPPT approach using Arduino board for photovoltaic systems

In photovoltaic (PV) systems, the conversion of solar energy into electrical energy by the PV module is influenced by various factors, including sunlight intensity and temperature. To achieve optimal performance, it is crucial to accurately track the maximum power point (MPP) of the PV module. Among the numerous MPP tracking (MPPT) techniques that have been developed, the perturbation and observation (PO) method has gained significant attention due to its simplicity and reliability. However, the fixed perturbation step size used in the traditional PO algorithm can result in poor tracking capability, excessive ripple, and drift, leading to high power loss and low tracking efficiency. To address these limitations, this paper proposes an improved version of the PO strategy that introduces an adaptable step-magnitude mechanism, utilizing current perturbation instead of the voltage perturbation typically employed in the classical PO method. This enhanced indirect PO approach maintains low complexity and can be easily implemented on a cost-effective Arduino Uno board. A streamlined C++ code integrates the improved PO MPPT strategy with a Proportional Integral Derivative (PID) controller, eliminating the need for separate PID blocks and further reducing system complexity. To evaluate the effectiveness of the proposed technique, comparative analyses are conducted against the traditional PO algorithm, particle swarm optimization (PSO), fuzzy logic control (FLC), and a recently introduced approach, the zone voltage (ZV) method. Simulation results using Proteus software demonstrate that the improved PO approach outperforms the other techniques in various aspects, including achieving the highest static and dynamic tracking efficiencies of 99.38% and 99.88%, respectively, negligible power loss and fluctuations, the fastest convergence speed, and the shortest tracking time of 0.19 seconds.

Balancing Efficiency and Accuracy: Enhanced Visual Simultaneous Localization and Mapping Incorporating Principal Direction Features

In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number of feature pyramid levels by 50%. Firstly, we innovatively incorporate the principal direction error, which represents the global geometric features of feature points, into the error function for pose estimation, utilizing Pareto optimal solutions to improve the localization accuracy. Secondly, for loop-closure detection, we construct a feature matrix by integrating the grayscale and gradient direction of an image. This matrix is then dimensionally reduced through aggregation, and a multi-layer detection approach is employed to ensure both efficiency and accuracy. Finally, we optimize the feature extraction levels and integrate our method into the visual system to speed up the extraction process and mitigate the impact of the reduced levels. We comprehensively evaluate the proposed method on local and public datasets. Experiments show that the SLAM method maintained high localization accuracy after reducing the tracking time by 24% compared with ORB SLAM3. Additionally, the proposed loop-closure-detection method demonstrated superior computational efficiency and detection accuracy compared to the existing methods.

An Adaptive Controller with Disturbance Observer for Underwater Vehicle Manipulator Systems

Dynamic control of underwater vehicle manipulator systems (UVMSs) is the key part of underwater intervention tasks. In this paper, we propose an adaptive controller with a disturbance observer that mainly consists of two parts: the first part is the adaptive control law that estimates the changes in the center of mass (COM) and the center of buoyancy (COB) of the vehicle, and the second part is the nonlinear disturbance observer that estimates the external disturbance and model uncertainties. To attenuate the overestimation problem, a damping term is introduced to the adaptive law. The stability of the proposed method is proven on the basis of Lyapunov theory. We develop three scenarios on the Simurv platform and illustrate the effectiveness of the proposed method with a short response time and high tracking performance.

A novel hybrid GMPPT method for PV systems to mitigate power oscillations and partial shading detection

The Maximum Power Point Tracking (MPPT) algorithm plays a crucial role in maximizing the power output of a PV system. While uniform shading conditions (USCs) present challenges related to dynamic changes in irradiance, partial shading conditions (PSCs) introduce the complexity of multiple local maxima and rapidly changing shading patterns. Conventional algorithms often struggle to effectively track the global maximum power point (GMPP) during PSCs leading to power losses and oscillations around the MPP. In contrast, advanced algorithms are more complex and require additional computational time causing the algorithm to unnecessarily scan the entire PV curve during USCs, thereby extending the tracking time. This paper proposes a hybrid approach that combines the Modified Perturb and Observe (MP&O) and Modified Coot Optimization Algorithm (MCOA) methods termed as MP&O-MCOA as a robust real time MPPT algorithm. The MP&O method with trapezoidal rule and adaptive step size is employed under USCs, while the MCOA with only one tuning parameter and fewer random numbers is utilized during PSCs to rapidly identify varying conditions. Experimental validation using a boost converter has demonstrated the effectiveness of the proposed method, achieving the fastest average tracking times of 0.24 s under USCs and 0.73 s under PSCs and highest average tracking accuracy above 99 % for all operating conditions tested. The proposed method has proven to outperform other existing MPPT methods under various weather conditions due to its ability to distinguish between USCs and PSCs at exceptional speed and accuracy.

Implementation of the MPPT Particle Swarm Optimization Algorithm on a Hybrid PV-TEG Solar Panel with a DC-DC Buck Converter

Abstract Solar panels necessitate power control to locate the optimal working point amidst quickly changing voltages and diverse weather circumstances. This enables the system to adapt and sustain optimal performance in real-time. In order to address this issue, the optimization methodology known as Maximum Power Point Tracking (MPPT) is employed using an algorithmic artificial intelligence (AI) method. Particle Swarm Optimization (PSO) is a swarm intelligence technique that has effectively tackled diverse optimization issues in intricate systems. The DC-DC Buck Converter, which incorporates Maximum Power Point Tracking (MPPT), serves as an interface between the load and the photovoltaic (PV) system to control the output voltage of the system. A comparison is made between the performance of the Maximum Power Point Tracking (MPPT) technique using the Particle Swarm Optimization (PSO) algorithm and the hill climbing (HC) algorithm developed through MATLAB/SIMULINK simulations. The findings indicate that the PSO algorithm exhibits superior performance in terms of tracking time, output power, and stability, with little fluctuations or noise, as compared to the HC method. The tracking time is 0.02 seconds and 2.52 seconds, respectively. The power at constant irradiation is 14.49 Watts and 14.43 Watts. The power at irradiation variation is 6.98 Watt and 6.96 Watt. The PSO algorithm achieved a remarkable accuracy of 99.76% in this investigation, surpassing that of HC. This enhancement makes the system more effective in acquiring maximum power values.

Trajectory Tracking Control of Unmanned Vehicles via Front-Wheel Driving

Automated Guided Vehicles (AGVs) are the fastest commercially available application of unmanned driving technology, and the research significance of unmanned vehicle technology remains substantial. This paper investigates the driving mode of AGVs and proposes a method to extend the kinematic model of center-driven unmanned vehicles to front-wheel drive. This change in driving force enables unmanned vehicles to achieve faster tracking and higher consistency, solving the problems of long tracking time and insufficient accuracy in complex environments and reducing production costs. By analyzing the posture relationship of the unmanned vehicle system during movement, we established a posture error system to analyze the trajectory tracking problem. Utilizing Lyapunov stability theory and the concept of backstepping, we designed a control scheme that uses linear velocity and heading angular velocity as variables for the posture error system. This control scheme aims to stabilize the system and achieve synchronized trajectory tracking control of the unmanned vehicle. The impact of control parameters in the controller on tracking performance is also discussed. The final experimental simulation results show that the system error stabilizes, and the unmanned vehicle accurately follows the predetermined trajectory, verifying the feasibility of our proposed method and control scheme.

Experimental validation of a novel hybrid Equilibrium Slime Mould Optimization for solar photovoltaic system

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT technique is a challenge faced by researchers due to factors such as fluctuations in irradiance and the presence of partial shading. This paper introduced a novel hybrid Equilibrium Slime Mould Optimization (ESMO) MPPT-based algorithm combining the advantages of two recent algorithms, Slime Mould Optimization (SMO) and Equilibrium Optimizer (EO). The ESMO algorithm is compared with highly efficient MPPT-based techniques such as SMO, EO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA), both under a Simulink environment and a real-time experimental laboratory setup using a Dspace1104 controller and PV emulator. The comparison focuses on performance under several irradiance cases, including instant irradiance change, partial shading, complex partial shading, and dynamic partial shading. The key advantage of ESMO is the fact that it has a single tunable parameter, which makes implementation much easier and, at the same time, reduces the computational resources that are required by the control system. Extensive testing proves the superiority of ESMO over all other techniques, the average efficiency of which is 99.98% under all conditions. Additionally, ESMO provides fast average tracking times of 244 ms under simulation experiments and 200 ms for real-time experiments. These results show that ESMO can be very important for future implementation in large-scale solar PV systems.

Intelligent beehive monitoring system based on internet of things and colony state analysis

Bees play a crucial role in terrestrial ecosystems. However, beekeepers are unable to monitor the state of beehives (bees and environment) all the time, which often results in bees escaping or even dying. Currently, some researchers provided the scheme of intelligent beehive monitoring system equipped with the Internet of Things (IoT), There remain two challenges: accurately monitor the environmental status around the hive and accurately track and monitor bees in real time. With the development of the IoT and computer vision algorithms, we hope to provide an automated and efficient system to meet the above challenges. In this paper, we proposed a hive monitoring system, and build a visualization module in the cloud to monitor the activity of bee colonies and the environmental dynamic changes. (1) We proposed a multi-bee tracking algorithm to solve the problem of monitoring bees at the door of the hive; (2) we constructed a dataset containing various complex scenes, named BEE22, for training and testing the performance of our algorithm; (3) we designed a bee counting rule, based on results of multi-bee tracking algorithm, to reasonably count the bees entering or leaving the beehive; (4) we have deployed multiple sensors around(center, margin, door, and environment) the hive to accurately reflect the changes in the environment around the hive. Experimental results demonstrate the effectiveness and excellence of our system. In particular, the tracking performance of the multi-bee tracking algorithm reaches 83.5 % ± 0.7 % Multiple Object Tracking Accuracy (MOTA) and 77.3 %±0.2 % Multiple Object Tracking Precision (MOTP), speeds up to 16 frames per second, compared with other algorithms, MOTA and Identity F1 Score (IDF1) are improved by 5.4 % and 8.2 % respectively. Moreover, our counting algorithm also achieved excellent results, with root mean square error (RMSE) of 1.3 ± 0.1, 0.2 ± 0.0, and 1.6 ± 0.1 in counting the number of bees current, entry, and out scene in an episode, respectively. After that, the system will be deployed and monitored for a long time in the actual scenario, it was found that the activity of bees decreased significantly under heavy rainfall conditions. Additionally, the activity of the bee colony will also increase accordingly, when the amplitude is 500 dB to 2000 dB, the temperature of the center of the beehive is 25 °C to 37 °C, and the humidity is 48 % to 67 %. In summary, our system can provide valuable information for bee farmers to make control decisions on hives.

Self- Control dan Internet addiction pada Mahasiswa: Pendekatan Structural Equation Modeling

The overuse of the internet is known as internet addiction. People who are addicted to the internet frequently require assistance in controlling their usage, which causes them to lose track of time and prefer the virtual world to the actual one. The purpose of this study is to look at how self-control affects college students' internet addiction. 352 students, aged between 18 and 24 (M = 20.46, SD = 1.31), living in Jakarta, Bogor, Depok, Tangerang, and Bekasi, made up the sample. There were 69 male students and 283 female students. Purposive sampling, a non-probability technique, was employed. Confirmatory factor analysis (CFA) is used in the construct validity assessment process. The structural equation modeling method is employed to test hypotheses. The results showed that data with an RMSEA value of 0.074 supported the fit model. In addition, there is a significant influence of self-control on internet addiction in students with an opposing direction of influence. The R-square of 40.2% obtains the amount of influence.