Time Tracking Research Articles

GEN Z’S IMPULSE BUYING BEHAVIOR OF SHOPPING THROUGH LIVE-STREAMING SOCIAL COMMERCE PLATFORMS:

In social commerce, consumers receive emotional motivation to purchase products because they feel familiar and bonded with the promoter, often losing track of time and self-awareness in a flow state, leading to impulse buying. As Gen Z are often presents more tendency toward impulse shopping behaviors compared to other generations, this study aims to investigate the impulse buying behavior of Generation Z (Gen Z) on live-streaming social commerce platforms, focusing on the roles of Parasocial Interaction (PSI) and Social Commerce Emotional Motivation (SCEM). Using a quantitative approach, data were collected via online surveys from individuals born between 1998 and 2007 who had made at least two unplanned purchases after watching live-streamed shopping events within four weeks. Statistical regression analysis was conducted using IBM SPSS 26.0 to explore the relationships between PSI, SCEM, and impulse buying behavior (IBB). The findings reveal that both PSI and SCEM significantly influence IBB among Gen Z consumers. Strong parasocial relationships with live-stream hosts enhance trust and connection, while emotional engagement through SCEM motivates spontaneous purchases. The study highlights the synergistic effect of PSI and SCEM, suggesting that a combined approach is most effective in driving impulse buys. This study contributes to the understanding of consumer behavior in the context of emerging social commerce trends, providing actionable insights for enhancing marketing strategies targeted at Gen Z.

A novel differentiated coverage-based lifetime metric for wireless sensor networks

This paper delves into optimizing network lifetime (NL) subject to connected-coverage requirement, a pivotal issue for realistic wireless sensor network (WSN) design. A key challenge in designing WSNs consisting of energy-limited sensors is maximizing NL, the time a network remains functional by providing the desired service quality. To this end, we introduce a novel NL metric addressing target-specific coverage requirements that remedies the shortcomings imposed by conventional definitions like first node die (FND) and last node die (LND). In this context, while we want targets to be sensed by multiple sensors for a portion of the network lifetime, we let the periods, during which cells are monitored by at least one sensor, vary. We also allow the ratios of multiple and single tracking times to differ depending on the target and incorporate target-based prioritization in coverage. Moreover, we address role assignment to sensors and propose a selective target-sensor assignment strategy. As such, we aim to reduce redundant data transmissions and hence overall energy consumption in WSNs. We first propose a unique 0-1 mixed integer programming (MIP) model, to analyze the impact of our proposal on optimal WSN performance, precisely. Next, we present comprehensive comparative studies of WSN performance for alternative NL metrics regarding different coverage requirements and priorities across a wide range of parameters. Our test results reveal that by utilizing our novel NL metric total coverage time can be improved significantly, while facilitating more reliable sensing of the target region.

Keeping track of time: An interaction of mossy fibers and climbing fibers

Precisely tracking time over second-long timescales is important for accurate anticipation and consequential actions, yet the neurobiological underpinnings remain unknown. In this issue of Neuron, Garcia-Garcia and colleagues1 show that computations in the cerebellum resulting from interactions between the mossy fiber and climbing fiber pathways contribute to long-interval learning during operant conditioning.

Experimental validation of a low-cost maximum power point tracking technique based on artificial neural network for photovoltaic systems

Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s.

Smart interactive whiteboard in EFL class at Islamic university

This research aims to investigate students’ perception of using Smart Interactive Whiteboard in the English as a foreign language (EFL) classroom in the State Islamic University in Jambi city. This research employs a quantitative survey method, utilizing a questionnaire as the primary data collection tool. The questionnaire consisted of 26 five-point Lickert-scale items in order to measure the students’ perceptions about (1) Perceived Learning Contribution, (2) Motivation, (3) Perceived Efficiency, and (4) Perceived Negative Effects. The results of the survey show that students like having smart, interactive whiteboards in the classroom. The results show that these technological tools help people learn, get motivated, feel like they are working well, and don't have any bad affects. The combination of audio and visual elements makes it easier to understand and provides a stimulating learning environment. Students also say they are more engaged, focused, involved, and excited about learning. Smart, interactive whiteboards are thought to make classrooms more visible, more organized, and better at keeping track of time. The fact that most students disagree with the idea that these tools have bad results shows that they like using them. Overall, the results show how smart interactive whiteboards can be benefited for positive learning experiences, motivation, efficiency, and student engagement in a positive and pleasant learning atmosphere.

Real Time Tracking of Nanoconfined Water-Assisted Ion Transfer in Functionalized Graphene Derivatives Supercapacitor Electrodes.

Water molecules confined in nanoscale spaces of 2D graphene layers have fascinated researchers worldwide for the past several years, especially in the context of energy storage applications. The water molecules exchanged along with ions during the electrochemical process can aid in wetting and stabilizing the layered materials resulting in an anomalous enhancement in the performance of supercapacitor electrodes. Engineering of 2D carbon electrode materials with various functionalities (oxygen (─O), fluorine (─F), nitrile (─C≡N), carboxylic (─COOH), carbonyl (─C═O), nitrogen (─N)) can alter the ion/water organization in graphene derivatives, and eventually their inherent ion storage ability. Thus, in the current study, a comparative set of functionalized graphene derivatives-fluorine-doped cyanographene (G-F-CN), cyanographene (G-CN), graphene acid (G-COOH), oxidized graphene acid (G-COOH (O)) and nitrogen superdoped graphene (G-N) is systematically evaluated toward charge storage in various aqueous-based electrolyte systems. Differences in functionalization on graphene derivatives influence the electrochemical properties, and the real-time mass exchange during the electrochemical process is monitored by electrochemical quartz crystal microbalance (EQCM). Electrogravimetric assessment revealed that oxidized 2D acid derivatives (G-COOH (O)) are shown to exhibit high ion storage performance along with maximum water transfer during the electrochemical process. The complex understanding of the processes gained during supercapacitor electrode charging in aqueous electrolytes paves the way toward the rational utilization of graphene derivatives in forefront energy storage applications.

An experiential account with recommendations for the design, installation, operation and maintenance of a farm-scale soil moisture sensing and mapping system

Context The research explores the benefits of real time tracking of soil moisture for various land management contexts and the importance of spatio-temporal modelling and mapping to gain clear and visual understanding of soil moisture fluxes across a farm. Aims This research aims to outline the key processes required for building an operational on-farm soil moisture monitoring system where the product is highly granular daily soil moisture maps depicting variations temporally, spatially and vertically. Methods We describe processes of capacitance soil moisture probe installation, data collection infrastructure, sensor calibration, spatio-temporal modelling, and mapping. Key results An out-of-bag soil moisture evaluation modelling system was tested for nearly 2 years. We found a model accuracy (RMSE) estimate of 0.002 cm−3 cm−3 and concordance of 0.96 were found. This result is averaged over this period but fluctuated daily, and related to rainfall patterns across the target farm, which were not directly incorporated into the modelling framework. As expected, incorporating prior estimates of soil moisture into the modelling framework contributed to very accurate estimates of real time available soil moisture. Conclusions This research promotes the importance of iterative improvements to the soil moisture monitoring system, particularly in areas of sensor recalibration and spatio-temporal modelling. We stress the need for a longer-term view and plan for ongoing maintenance and improvement of such systems in the emerging digital farming ecosystem. Implications The results of this research will be useful for researchers and practitioners involved in the design and implementation of on-farm soil monitoring systems.

Cost-effectiveness of train-the-trainer versus expert consultation training models for implementing interpersonal psychotherapy in college mental health settings: evidence from a national cluster randomized trial

BackgroundThis study is a cost-effectiveness study of two implementation strategies designed to train therapists in college and university counseling centers to deliver interpersonal psychotherapy. Costs of implementing a train-the-trainer (TTT) strategy versus an expert consultation strategy were estimated, and their relative effects upon therapist outcomes were calculated and compared.MethodsTwenty four counseling centers were recruited across the United States. These centers were randomized to either a TTT (experimental) condition, in which an in-house therapist trained other center therapists, or an expert consultation condition, in which center therapists participated in a workshop and received 12 months of ongoing supervision. The main outcome was therapist fidelity (adherence and competence) to interpersonal psychotherapy, assessed via audio recordings of therapy sessions, and analyzed using linear mixed models. Costs of each condition were quantified using time-driven activity-based costing methods, and involved a costing survey administered to center directors, follow up interviews and validation checks, and comparison of time tracking logs of trainers in the expert condition. Mean costs to produce one therapist were obtained for each condition. The costs to produce equivalent improvements in therapist-level outcomes were then compared between the two conditions.ResultsMean cost incurred by counseling centers to train one therapist using the TTT strategy was $3,407 (median = $3,077); mean cost to produce one trained therapist in the control condition was $2,055 (median = $1,932). Therapists in the TTT condition, on average, demonstrated a 0.043 higher adherence score compared to therapists in the control condition; however, this difference was not statistically significant. For the competence outcome, effect size for therapists in the TTT condition was in the large range (1.16; 95% CI: 0.85–1.46; p < .001), and therapists in this condition, on average, demonstrated a 0.073 higher competence score compared to those in the expert consultation condition (95% CI, 0.008–0.14; p = .03). Counseling centers that used the TTT model incurred $353 less in training costs to produce equivalent improvements in therapist competence.ConclusionsDespite its higher short run costs, the TTT implementation strategy produces greater increases in therapist competence when compared to expert consultation. Expanding resources to support this platform for service delivery can be an effective way to enhance the mental health care of young people seeking care in college and university counseling centers.Trial registrationClinicalTrials.gov Identifier: NCT02079142.

An improved P&O-based DC-link voltage control algorithm for standalone PV systems with adaptive step-size and operating point determination strategy

ABSTRACT An improved DC-Link voltage controller (IPOVR), which is built upon the perturb & observe-based voltage regulator (POVR), is proposed in this paper. The aim is to achieve the best balance between tracking time and steady-state oscillation while maintaining good tracking accuracy. This is achieved by implementing a dynamic step-size (∆D) changing strategy and operating region detection technique. Quick tracking time is produced with large initial ∆D, and low steady-state oscillation is obtained through continuous ∆D reduction. Additionally, through the operating region determination technique, the IPOVR operates over a much wider range, typically with lower PV current (IPV), which helps in reducing the losses of the system. It is found that the IPOVR has the lowest steady-state ripple and the highest tracking accuracy, which are averaged at 0.44% and 99.98%, respectively. In terms of tracking time, the IPOVR is averaged at 0.15 s, which is only 0.07 s slower compared to the POVR with high ∆D but is 0.19 s faster than the POVR with low ∆D. Furthermore, the IPOVR recorded higher efficiencies under all tested conditions compared to the POVR. The IPOVR is also demonstrated to be working decently when being connected to a multilevel inverter to generate AC power.

Application of circle search algorithm for solar PV maximum power point tracking under complex partial shading conditions

Solar photovoltaic (SPV) energy exhibits a potential role in the world for generating electricity and avoiding the block out in the power system. However, the hardest task in the PV system is to track the global maxima power (GPP) instead of local peak power (LPP) under partial shading conditions (PSC). To track the GPP, a new circle search algorithm (CSA) is implemented in this paper and is executed in MATLAB/SIMULINK environment. This method is examined at various irradiation conditions and compared their performance with other studied methods in terms of tracking time, settling time and efficiency. The simulation outcome of the CSA method efficiencies 99.96 % in case 1, 99.74 % in case 2, and 99.94 % in case 3. For checking the robustness and stability of the system statistical analysis was done on the proposed methods. It is found that the suggested method exhibits higher performance than the other methods.

A fresh look at the concept of a relativistic oscillator

The theory of a unidimensional relativistic oscillator is developed assuming a frame of reference fixed to the oscillator. This approach is at variance with conventional consideration of the same problem in an inertial frame of reference. It is shown that examination of a freely falling body by an observer sitting on the oscillator is advantageous because it permits to avoid introducing of any additional conditions required to ensure the Lorentz invariance of the theory. The time track of such a body in a rigid accelerated frame of reference is derived in a general manner. The effect of a systematic delay of time during vibrations is unveiled in the ultrarelativistic regime.

Relationship between visual ability assessment and punch performance in competition in male amateur boxers.

Sport visual ability plays an important role in the performance of elite athletes in competition. However, its relationship with boxers' performance has not been fully understood. This study investigated the relationship between sports visual abilities and in-competition punching performance in 26 highly trained male amateur boxers. Ten visual abilities of the boxers were tested using the Senaptec Sensory Station (Senaptec, Beaverton, OR, United States), including Visual Clarity (VC), Contrast Sensitivity (CS), Depth Perception (DP), Near Far Quickness (NFQ), Target Capture (TC), Perception Span (PS), Multiple Object Tracking (MOT), Eye-Hand Coordination (EHC), Go/No Go (GNG), and Reaction Time (RT). Performance analyses were conducted on national boxing championships conducted by the boxers to analyze their punching accuracy. Correlation and regression analyses showed that punch performance %Hit was very strong correlated with DP, EHC, GNG, and RT, and showed a strong correlation with VC, CS, and PS. %Hit was moderate correlated with MOT, while there was no correlation with NFQ and TC. In addition, RT, EHC, and DP are important visual ability variables for boxers. The results of this study indicate that there is an overall relationship between sports visual ability and boxing performance, but there is also a specific relationship between variables in visual ability and boxing performance, especially faster reaction times, better processing of visual information, and decision-making abilities, and the ability to accurately recognize the distance and position of an opponent and their punches are essential for enhancing boxing performance. Further studies are needed to investigate the relationship between sport visual ability and more comprehensive performance in boxers, and the possibility of enhancing performance through specific visual training.

Event Vision-based Corner Detection with Count-normalized Multi-Layer Perceptron and Throughput Indicator

The advancement of event cameras has sparked a revolution in imaging technology, presenting exciting opportunities for vision-based measurement tasks. Event cameras operate on an innovative asynchronous imaging principle, which offers several advantages over traditional cameras, including ultra-high dynamic range and exceptional temporal resolution. As a result, event cameras excel in challenging environments characterized by motion blur, overexposure, or underexposure, outperforming conventional frame-based cameras. However, the asynchronous signal streams generated by event cameras pose compatibility challenges with existing vision-based measurement algorithms. This paper focuses specifically on corner detection, a critical vision-based measurement task, tailored for event cameras. To address this challenge, we propose novel corner detectors that leverage advanced optimization techniques, including enhanced time surface representations, multi-layer perceptron classifiers, and an innovative throughput mechanism. Through rigorous experimentation, our method consistently shows lower projection errors compared to state-of-the-art methods across all datasets while also maintaining longer tracking times in low-textured scenarios. Specifically, our CMLP and CMLP-T methods achieve an average valid tracking rate of 83.38% and 84.65%, respectively, on the DAVIS240C dataset collection, surpassing all existing methods. We validate the effectiveness of our proposed corner detectors by demonstrating their enhanced performance compared to state-of-the-art methods. Furthermore, our work contributes to the application of machine learning in event signal processing for vision-based measurement tasks, providing insights into optimizing models for the unique characteristics of event cameras.

Analisis Perilaku Sosial Santri sebagai Dampak dari Penggunaan Gadget di Pondok Pesantren Manbaul Hikam Desa Tegalmojo Kecamatan Tegalsiwalan Kabupaten Probolinggo

This research aims to look at the phenomenon of gadget use and its impact on the social behavior of children at the Manbaul Hikam Islamic Boarding School in Tegalmojo Village, Tegalsiwalan District, Probolinggo Regency. This type of research is qualitative research with descriptive methods. The selection of informants was carried out using Purposive Sampling technique. The type of data is collected through observation, interviews and documentation. The research results show that some students use gadgets. The features that are often used are camera, WA, games, Mp3, YouTube, internet and social media. These features are used to find the information needed, search for study materials, for entertainment, and to relieve boredom. Gadgets are always used all the time and are often used at inappropriate times, such as in the learning process of students using gadgets without the teacher's knowledge. The phenomenon of using gadgets has an impact on the social behavior of students at the Manbaul Hikam Islamic Boarding School in Tegalmojo Village, namely making it easy for students to interact with many people via social media so that they lose track of time and often do not participate in Islamic Boarding School activities.

Analisis dan Pemodelan Proses Bisnis Katering pada UMKM Menggunakan BPMN

Fafiras Kitchen is a family business that focuses on food catering services. This business involves three main operational stages, namely the ordering process, procurement of raw materials, and delivery of products to customers. From the results of observations and interviews, several problems were identified such as the absence of good records in the ordering process resulting in errors in order fulfillment and loss of customer data, dependence on one raw material supplier increases the risk of delays and disruptions in the supply chain and inefficient delivery management caused by human error and no real time tracking, so this has an impact on customer loyalty. This research aims to analyze and model the current business process and provide recommendations for improvement. Modeling is done using Business Process Model and Notation (BPMN) with a qualitative approach to investigate existing business processes. The results of this research can identify areas of improvement to increase the efficiency and effectiveness of MSMEs operations so that MSMEs can better understand, manage, and optimize their business processes. In addition, this research also conducted simulations using the Bizagi Modeler application to test the validity of the business process modeling that has been made. The simulation results show an increase in time in the implementation of business processes.

Highly sensitive volumetric single-molecule imaging.

Volumetric subcellular imaging has long been essential for studying structures and dynamics in cells and tissues. However, due to limited imaging speed and depth of field, it has been challenging to perform live-cell imaging and single-particle tracking. Here we report a 2.5D fluorescence microscopy combined with highly inclined illumination beams, which significantly reduce not only the image acquisition time but also the out-of-focus background by ∼2-fold compared to epi-illumination. Instead of sequential z-scanning, our method projects a certain depth of volumetric information onto a 2D plane in a single shot using multi-layered glass for incoherent wavefront splitting, enabling high photon detection efficiency. We apply our method to multi-color immunofluorescence imaging and volumetric super-resolution imaging, covering ∼3-4 µm thickness of samples without z-scanning. Additionally, we demonstrate that our approach can substantially extend the observation time of single-particle tracking in living cells.

Impact of Data Assimilation in Sensitive Features on the Predictability of the 2012 Great Arctic Cyclone

AbstractThe Great Arctic Cyclone 2012 (AC12) is used to understand the role of initial condition errors in the predictability at 2–3‐day forecast range of a high‐impact summer Arctic Cyclone (AC). Ensemble sensitivity analysis (ESA) is first performed to identify potentially sensitive regions of the cyclone evolution using an ensemble baseline forecast with conventional in situ observations assimilated. A pseudo‐observation method is then introduced to investigate impacts of hypothetical observations in these sensitive but unobserved regions. In the baseline experiments with in situ observations assimilated, the forecasted AC12 reaches its peak intensity 18 hr earlier than in the verifying Global Forecast System Analysis (GFS‐ANL) and the cyclone track is biased toward the southwest. Using ESA, the time of peak intensity and the cyclone track error are identified to be sensitive to the upstream trough, downstream ridge, and the tropopause polar vortex (TPV) to the northeast (NE TPV) of the AC12. These features were not observed by the in situ observation networks. To examine the impact of the observation gaps, pseudo‐observations drawn from GFS‐ANL are assimilated. Pseudo‐observations sample the three features separately to study the impact of the initial condition error on the predictability of AC12. The cyclone peak intensity timing error and track error are greatly reduced when the initial condition error is reduced near the NE TPV. A southward expansion of the NE TPV and the corresponding southward shifting low‐level front lead the forecasted AC12 to progress to the east, which better agrees with the verifying GFS‐ANL.

Development of a multi-level feature fusion model for basketball player trajectory tracking

To solve the problems of low matching degree, long tracking time, and low accuracy of multi-target tracking in the process of athlete motion trajectory tracking using deep learning technology, a new athlete motion trajectory tracking model was proposed in this study. The study first optimized the current object detection algorithm in basketball, utilized a hybrid attention mechanism to extract object features, and improved the non-maximum suppression strategy. Then, a hybrid branch network was introduced to improve the residual network and a new athlete identity recognition model was proposed. Finally, a new trajectory tracking model was designed by combining the object detection model and the athlete identity recognition model. The research results indicated that in the object detection experiment, the detection time of the proposed object detection algorithm was always below 0.4 s, and its average accuracy reached up to 0.63. In trajectory tracking testing, the final built tracking model had a multi-target tracking accuracy of up to 0.98, and its tracking overlap rate was as low as 0.02. This study has the following two contributions. Firstly, a new model of athlete trajectory tracking is proposed, which improves the accuracy and efficiency of multi-target tracking by optimizing object detection algorithm and introducing hybrid branch network. Second, the model has excellent performance in both object detection and track tracking, which can not only provide a new solution for athletes' motion trajectory tracking, but also significantly improve the effect of motion tracking.

Short communication: Inverse correlation between radiation damage and fission-track etching time on monazite

Abstract. In this study, we explored the impacts of radiation damage and chemical composition on the etching time of fission tracks in monazite. Despite the potential of monazite fission-track (MFT) dating as an ultralow-temperature thermochronology, the comprehensive effects of radiation damage and non-formula elements, especially on the etching rate of MFTs, remain unexplored, and established analytical procedures are lacking. We quantified the degree of radiation damage (ΔFHWM) in Cretaceous to Quaternary monazites distributed in the Japan arc through Raman spectroscopy and chemical composition analyses. Subsequently, MFT etching was performed to examine the correlation between these parameters and the etching time. Estimation of the degree of radiation damage showed an increase in radiation damage corresponding to the cooling age of each geological unit. For example, the Toya ignimbrite (ca. 0.1 Ma) and the Kurobegawa granodiorite (&lt; 0.8 Ma), both of which are types of monazite from Quaternary geological units, have ΔFHWM values of 0.27 and 0.55 cm−1, respectively. In contrast, the Muro ignimbrite (ca. 15 Ma) has a ΔFHWM value of 4.01 cm−1, while the Kibe granite and the Sagawa granite, both of which are Cretaceous granitoids, yielded 7.35 and 6.31 cm−1, respectively. MFT etching of these samples according to the existing recipe (6 M HCl at 90 °C for 60–90 min) was completed at 1200, 860, 210, 120, and 90 min for the Toya ignimbrite, Kurobegawa granodiorite, Muro ignimbrite, Sagawa granite, and Kibe granite, respectively. These outcomes highlight an inverse relationship between MFT etching time and the degree of radiation damage in monazite, while the correlation between MFT etching time and chemical composition was unclear. The results affirm earlier considerations that the etching rate of MFTs is strongly influenced by radiation damage. Conversely, young samples with lower levels of radiation damage exhibit higher chemical resistance, suggesting that existing etching recipes may not adequately etch MFTs.

DESIGN AND EVALUATION OF A LOW COST BIOMETRIC TIME TRACKING SYSTEM

Employee time tracking is sometimes an issue for many companies for two reasons. First, the lack of convenient devices. Second, the reluctance of employees. This is particularly noticeable in situations where employees do not work within corporate facilities, making it difficult to track work hours. Recent regulations by governments have emphasized the need for accurate time tracking, creating uncertainty in business environments. This paper proposes a simple and cost-effective time tracking system using the Asterisk PBX that runs on commodity hardware. This allows employees to track their working time without additional devices and regardless their locations. This solution uses an interactive voice menu, so employees may record actions related to their daily activities (start and end of the workday, etc.). The employee calls the Asterisk PBX and utters a passphrase before recording the action. Then, a biometric module analyzes the employee's voice to verify their identity, preventing impersonation. The verification process is independent of the text (IT), so the passphrases given to employees have no constraints. The results of the tests performed on the system indicate an accuracy of 99.7%, demonstrating the reliability of the biometric module. High values of accuracy (99.89) and recall (99.8%) are also obtained. Thus, the identification of legitimate employees and fraudulent identification attempts are very accurate.