Alireza Soleymanipoor, Tomoyoshi Maeno, Kosuke Tosaka
et al.
Frictional conditions at the workpiece–die interface are critical in metal forming, as significant plastic deformation generates heat that affects lubricant performance. Understanding lubricant behavior, especially its influence on friction under elevated temperatures, is essential for optimizing forming processes and meeting ecological demands. While the conventional ring compression test evaluates friction through inner diameter changes, it becomes unreliable when friction is transient. In this study, a warm ring compression test incorporating an in situ measurement system is proposed to evaluate the transient frictional behavior of lubricants under temperature rise due to plastic deformation. Results show that at <i>T</i> = 50 °C and 150 °C, the friction coefficient increases notably with the compression ratio, whereas at <i>T</i> = 100 °C, it remains relatively stable. This stability is likely due to the optimal performance of the chlorinated base lubricant at 100 °C, where boundary lubrication is most effective. At <i>T</i> = 50 °C, the additive activation is insufficient, and at <i>T</i> = 150 °C, thermal degradation may reduce its effectiveness. Finite element simulations using the transient friction coefficient reproduce the deformed ring cross-section with high accuracy, while those using constant friction values show less agreement.
I investigate how partial prey migration cycles, analogous to a non-autonomous harmonic oscillator, force the classical Lotka-Volterra model and reshape predator-prey interactions. A 3D nonlinear system is introduced, into which the external forcing replicates the entry and exit of partial migrants from the ecosystem, devoid feedback loops. Numerical simulations reveal an elusive resilience contour of the species interplay under stationary migration cycles. Thus, quasi-periodic and chaotic fluctuations appear at a minimum migration magnitude, vanishing beyond a bifurcation-induced tipping point. However, resilient interactions surge in localized hotspots, i.e., narrow regions of phase space and forcing intensity. It is striking to note that the detected chaos exhibits a threefold complexity related to migration magnitude, initial conditions, and a functional response parameter, implying a basin of attraction intertwined at fractal boundaries. In contrast, the resilience non-monotonicity fades due to ascending cycles of partial prey migration involving recruitment of a cohort of migrants by its resident species. In this case, chaos is suppressed, leading to predictable oscillations and phase-locking. Even extreme predator-prey ratios (e.g., 10:1) do not endanger prey. Despite its parsimony, the framework offers a tractable prototype with broader ecological applicability for studying how exogenous forcings (e.g., climate-driven phenology), can alter ecosystems.
The development of communication technology will promote the application of Internet of Things, and Beyond 5G will become a new technology promoter. At the same time, Beyond 5G will become one of the important supports for the development of edge computing technology. This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing. Through trial and error learning of agent, the optimal spectrum and power can be determined for transmission without global information, so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure. The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.
Rizki Aulia Putra, Rice Novita, Tengku Khairil Ahsyar
et al.
Google Play Store is the official app store for Android devices from Google that offers rating and review features. This feature on the platform is a source of data for sentiment analysis in research on app user satisfaction. The purpose of this study is to provide an overview of app user satisfaction and evaluate the accuracy of the algorithms used. The algorithms compared include Support Vector Machine (SVM), namely linear, rbf, sigmoid, and polynomial kernels with Naïve Bayes Classifier (NBC). The key variables analyzed include perceived usefulness, perceived ease of use, relia-bility, responsiveness, and website design. The results showed that the SVM algorithm with a linear kernel achieved the highest accuracy of 95.23% compared to the NBC algorithm of 91.43%. For other accuracy results, rbf kernel 94.35%, sigmoid kernel 95.19% and polynomial kernel 93.31%. In addition, the results of sentiment analysis on application user satis-faction revealed that 75% of users were dissatisfied, with the service indicator having the highest number of negative sen-timents. These findings suggest that sentiment analysis can be an effective tool for companies to measure and improve user satisfaction. In addition, these results can also be a useful reference for new users in assessing apps before using them.
Jan Kinne, Robert Dehghan, Sebastian Schmidt
et al.
While many digital technologies provide opportunities for creating business models that impact sustainability, some technologies, especially blockchain applications, are often criticized for harming the environment, e.g. due to high energy demand. In our study, we present a novel approach to identifying sustainability-focused blockchain companies and relate their level of engagement to location factors and entrepreneurial ecosystem embeddedness. For this, we use a large-scale web scraping approach to analyze the textual content and hyperlink networks of all US companies from their websites. Our results show that blockchain remains a niche technology, with its use communicated by about 0.6% of US companies. However, the proportion of blockchain companies that are committed to sustainability is significantly higher than in the overall firm population. Additionally, we find that such sustainability-engaged blockchain companies have, at least quantitatively, a more intensive embedding in entrepreneurial ecosystems, while infrastructural and socio-economic location factors hardly play a role.
This paper presents a backpropagation neural network (BPNN) approach based on the sparse autoencoder (SAE) for short-term water demand forecasting. In this method, the SAE is used as a feature learning method to extract useful information from hourly water demand data in an unsupervised manner. After that, the extracted information is employed to optimize the initial weights and thresholds of the BPNN. In addition, to enhance the effectiveness of the proposed method, data reconstruction is implemented to create suitable samples for the BPNN, and the early stopping method is employed to overcome the BPNN overfitting problem. Data collected from a real-world water distribution system are used to verify the effectiveness of the proposed method, and a comparison with the BPNN and other BPNN-based methods which integrate the BPNN with particle swarm optimization (PSO) and the mind evolutionary algorithm (MEA), respectively, is conducted. The results show that the proposed method can achieve fairly accurate and stable forecasts with a 2.31% mean absolute percentage error (MAPE) and 320 m3/h root mean squared error (RMSE). Compared with the BPNN, PSO–BPNN and MEA–BPNN models, the proposed method gains MAPE improvements of 5.80, 3.33 and 3.89%, respectively. In terms of the RMSE, promising improvements (i.e., 5.27, 2.73 and 3.33%, respectively) can be obtained.
HIGHLIGHTS
To enhance the performance of the BPNN, the SAE is introduced to extract useful features in an unsupervised manner.;
An effective framework which integrates the BPNN with the SAE and early stopping technique is proposed for water demand forecasting.;
The proposed method is verified by comparing with the BPNN and similar methods which integrate the BPNN with PSO and the MEA, respectively.;
Information technology, Environmental technology. Sanitary engineering
In addition to the benefits of hybrid phase shift keying (HPSK) modulation in reducing the peak to average power ratio of the transmitted signal to reduce the zero cross- ings and the 0◦-degree phase transmissions, HPSK enhances the bit error rate (BER) measure of the signal performance. The constellation of the HPSK is analyzed, and an expression for the conditional probability of HPSK modulation over additive white Gaussian noise (AWGN) is derived. This BER measure of HPSK is shown to outperform quadrature phase shift keying (QPSK) modulation. HPSK performance through Nakagami – m fading channel is also considered.
Farassulthana Azzahra Willary Yaasiin, Herman Tolle, Hanifah Muslimah Az-Zahra
Bimbingan akademik merupakan kegiatan konsultasi antara dosen penasihat akademik dan mahasiswa dalam membantu menyelesaikan masalah studi serta merencanakan studi sesuai dengan minat dan kemampuannya. Panduan Standard Operating Procedure (SOP) yang baru di Fakultas Ilmu Komputer (FILKOM) telah dikembangkan secara khusus agar dosen dan mahasiswa dapat secara berkala memantau perkembangan studi mahasiswa dan melihat kekurangan studi mahasiswa berbasiskan pada data analisis hasil studi mahasiswa. Proses evaluasi berbasiskan data akan lebih mudah dilakukan jika menggunakan suatu sistem atau aplikasi yang memiliki visualisasi data yang bersesuaian. Untuk itulah perlu dirancang pengalaman pengguna dari aplikasi Pembimbingan PA agar dapat menjadi acuan dalam pengembangan sistem nantinya. Penelitian ini membuat perancangan user experience aplikasi bimbingan akademik mahasiswa FILKOM dengan menerapkan metode Human-Centered Design (HCD) untuk membantu mengembangkan desain solusi yang fokus pada perspektif manusia ke dalam semua bagian proses pemecah permasalahan agar dapat membantu memetakan kebutuhan yang tepat bagi stakeholder dan pengguna. Hasil pengujian dengan metode usability testing menggunakan kombinasi penilaian pengujian ISO 9241-210 dan UEQ dengan detail teknik penilaian completion rate, time based efficiency, System Usability Scale (SUS), dan User Experience Questionnaire (UEQ).
Abstract
Academic guidance is a consultation activity between academic advisory lecturers and students in helping to solve study problems and planning studies according to their interests and abilities. The new Standard Operating Procedure (SOP) guidelines at the Faculty of Computer Science (FILKOM) have been specially developed so that lecturers and students can periodically monitor the progress of student studies and see the shortcomings of student studies based on data analysis of student study results. The data-based evaluation process will be easier to do if you use a system or application that has the appropriate data visualization. For this reason, it is necessary to design the user experience of the PA Guidance application so that it can be a reference in the development of the system later. This study designed a user experience application for academic guidance for FILKOM students by applying the Human-Centered Design (HCD) method to help develop solution designs that focus on the human perspective into all parts of the problem-solving process in order to help map out the right needs for stakeholders and users. The test results using the usability testing method use a combination of ISO 9241-210 and UEQ testing assessments with detailed assessment techniques for completion rate, time based efficiency, System Usability Scale (SUS), and User Experience Questionnaire (UEQ).
In this paper, an eco-epidemiological model has been studied where disease of prey population is modelled by a Susceptible–Infected (SI) scheme. Prey switching strategy is adopted by predator population when they are provided with two types of prey, susceptible and infected prey. However switching may happen due to several reasons such as shortage of preferable prey or risk in hunting the plentiful prey. In this work, we have proposed a prey–predator system with a particular type of switching functional response where a predator feeds on susceptible and infected prey but it switches from one type of prey to another when a particular prey population becomes lower. Both the species are supposed to be commercially viable and undergo constant non-selective harvesting. The stability aspects of the switching models around the infection-free state from a local as well as a global perspective has been investigated. Our aim is to study the role of harvesting and refuge of susceptible population on the dynamics of disease propagation and/or annihilation of an epidemiological model under consideration of switching phenomena. Numerical simulations are done to demonstrate our analytical results.
Sinan S. Mohammed Sheet, Tian-Swee Tan, M.A. As’ari
et al.
Retinal tissue plays a crucial part in human vision. Infections of retinal tissue and delayed treatment or untreated infection could lead to loss of vision. Additionally, the diagnosis is prone to errors when huge dataset is involved. Therefore, a fully automated model of identification of retinal disease is proposed to reduce human interaction while retaining its high accuracy classification results. This paper introduces an enhanced design of a fully automatic multi-class retina diseases prediction system to assist ophthalmologists in making speedy and accurate investigation. Retinal fundus images, which have been used in this study, were downloaded from the stare website (157 images from five classes: BDR, CRVO, CNV, PDR, and Normal). The five files were categorized according to their annotations conducted by the experienced specialists. The categorized images were first processed with the proposed upgraded contrast-limited adaptive histogram filter for image brightness enhancement, noise reduction, and intensity spectrum normalization. The proposed model was designed with transfer learning method and the fine-tuned pre-trained RESNET50. Eventually, the proposed framework was examined with performance evaluation parameters, recorded a classification rate with 100% sensitivity, 100% specificity, and 100% accuracy. The performance of the proposed model showed a magnificent superiority as compared to the state-of-the-art studies.
Rao Sanjana, Mohanty Sattwik, Amarnani Chirag
et al.
A blood glucose metre is a small, portable device that measures blood glucose levels. To avoid long-term complications from diabetes, careful blood glucose control is required. New blood glucose metres have a small size, a large memory capacity, blood glucose manipulation techniques, and computer-based data processing capabilities.
The Glucose Detection System is implemented in tandem with an improved Op-based Potentiostat and an automatic test system. This project's goal is to investigate an improved OP-based Three-Electrode Potentiostat that can be used in an Electrochemical Glucose Biosensor device to achieve comparable measurement accuracy. This Project enables us to make the Glucometer Cost-Efficient, Affordable and Easy to Use. They hold a lot of promise in the field of bio-detection on the go systems for use in health care and biomedicine.
Kim Phuong Dao, Katrien De Cocker, Huong Ly Tong
et al.
BackgroundHealthy behaviors are crucial for maintaining a person’s health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life.
ObjectiveThis systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives.
MethodsWe searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies.
ResultsWe included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive (P<.001) and psychotic symptoms (P=.03), drinking frequency (P<.001), and eating patterns (P=.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, hampering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs.
ConclusionsThe use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots).
Trial RegistrationPROSPERO CRD42019138739; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=138739
Information technology, Public aspects of medicine
Junaidi Junaidi, Prasetyo Wibowo, Dini Yuniasri
et al.
<p>A common way to maintain the quality of service on systems that are growing rapidly is by increasing server specifications or by adding servers. The utility of servers can be balanced with the presence of a load balancer to manage server loads. In this paper, we propose a machine learning algorithm that utilizes server resources CPU and memory to forecast the future of resources server loads. We identify the timespan of forecasting should be long enough to avoid dispatcher's lack of information server distribution at runtime. Additionally, server profile pulling, forecasting server resources, and dispatching should be asynchronous with the request listener of the load balancer to minimize response delay. For production use, we recommend that the load balancer should have friendly user interface to make it easier to be configured, such as adding resources of servers as parameter criteria. We also recommended from beginning to start to save the log data server resources because the more data to process, the more accurate prediction of server load will be.</p>
Abstract Context: Big Data systems are a class of software systems that ingest, store, process and serve massive amounts of heterogeneous data, from multiple sources. Despite their undisputed impact in current society, their engineering is still in its infancy and companies find it difficult to adopt them due to their inherent complexity. Existing attempts to provide architectural guidelines for their engineering fail to take into account important Big Data characteristics, such as the management, evolution and quality of the data. Objective: In this paper, we follow software engineering principles to refine the λ -architecture, a reference model for Big Data systems, and use it as seed to create Bolster , a software reference architecture (SRA) for semantic-aware Big Data systems. Method: By including a new layer into the λ -architecture, the Semantic Layer, Bolster is capable of handling the most representative Big Data characteristics (i.e., Volume, Velocity, Variety, Variability and Veracity). Results: We present the successful implementation of Bolster in three industrial projects, involving five organizations. The validation results show high level of agreement among practitioners from all organizations with respect to standard quality factors. Conclusion: As an SRA, Bolster allows organizations to design concrete architectures tailored to their specific needs. A distinguishing feature is that it provides semantic-awareness in Big Data Systems. These are Big Data system implementations that have components to simplify data definition and exploitation. In particular, they leverage metadata (i.e., data describing data) to enable (partial) automation of data exploitation and to aid the user in their decision making processes. This simplification supports the differentiation of responsibilities into cohesive roles enhancing data governance.
Constantly exposing individuals to high levels of stress can have a lot of health consequences.
This is mainly caused by an individual not being correctly prepared, or due to the lack of appropriate
support or skills required, in order to cope with requests or demands. This leads to growing problems
not being solved soon enough, and the person ends up in a state of high emotional tension. All internal
bodily systems become exhausted, which can result in illness and diseases. Due to problems coping with
difficult or new tasks at work, self-esteem is lowered, which can adversely affect both the employee
and the employer, which in turn affects the entire workplace. For that reason, the aim of this article is
to communicate the research results of the professional burnout phenomenon, based on the analysis of
selected literature concerning the subject.
Management. Industrial management, Management information systems
Suen, Lorna, Wang, Wenru, Cheng, Kenneth King Yip
et al.
BackgroundObesity is a common global health problem and increases the risk of many chronic illnesses. Given the adverse effects of antiobesity agents and bariatric surgeries, the exploration of noninvasive and nonpharmacological complementary methods for weight reduction is warranted.
ObjectiveThe study aimed to determine whether self-administered auricular acupressure (AA) integrated with a smartphone app was more effective than using AA alone or the controls for weight reduction.
MethodsThis study is a 3-arm randomized waitlist-controlled feasibility trial. A total of 59 eligible participants were randomly divided into either group 1 (AA group, n=19), group 2 (AA plus smartphone app, n=19), or group 3 (waitlist control, n=21). A total of 6 reflective zones or acupoints for weight reduction were chosen. The smartphone app could send out daily messages to the subjects to remind them to perform self-pressing on the 6 ear acupoints. A “date picker” of the 8-week treatment course was used to enable the users to input the compliance of pressing and the number of bowel movement daily instead of using the booklet for recordings. The app also served as a reminder for the subjects regarding the dates for returning to the center for acupoint changing and assessments. Treatment was delivered 2 times a week, for 8 weeks. Generalized estimating equations were used to examine the interactions among the groups before and after intervention.
ResultsSubjects in group 2 expressed that the smartphone app was useful (7.41 out of 10). The most popular features were the daily reminders for performing self-pressing (88%), the ear diagram indicating the locations and functions of the 6 ear points (71%), and ear pressing method demonstrated in the video scripts (47%). Nearly 90% of the participants completed the 8-week intervention, with a high satisfaction toward the overall arrangement (8.37 out of 10). The subjects in group 1 and 2 achieved better therapeutic effects in terms of body weight, body mass index (BMI), waist circumference, and hip circumference and perceived more fullness before meals than the waitlist controls. Although no significant differences in the pairwise comparisons between the 2 groups were detected (P>.05), the decrease in body weight, BMI, body fat, visceral fat rating and leptin level, and increase in adiponectin level were notable in group 2 before and after the intervention.
ConclusionsThe high compliance rate and high satisfaction toward the trial arrangement indicate that AA can be used to achieve weight reduction and applied in future large-scale studies. AA integrated with the smartphone app has a more notable effect than using AA alone for weight reduction. Larger sample size should be considered in future trials to determine the causal relationship between treatment and effect.
Trial RegistrationClinicalTrials.gov NCT03442712; https://clinicaltrials.gov/ct2/show/NCT03442712 (Archived by WebCite at http://www.webcitation.org/78L2tO8Ql)
Information technology, Public aspects of medicine