Miracle-dz/UniFAR-FTUs huggingface dataset:
J urnal M anajemen (E disi E lektronik )

Today, washing clothes is a separate problem for some people. This is because not a few people have very busy activities in their daily lives. Indirectly, they decided to use a laundry service which was considered fast and efficient due to the limited time they had. Self-service laundry service is a laundry service that has a customer service system that will wash their own clothes at the laundry in a short time. Self-service based methods have developed very rapidly in various business sectors. This study purpose is to determine the level of customer satisfaction and their level of loyalty to self-service-based laundry services. This study uses the SEM-PLS analysis method. Data is obtained through observation with business owners/supervisors in order to understand the patterns and mechanisms of implemented marketing communications. As well as distributing online questionnaires based on The Flower of Service which were filled out by 302 respondents who then drew 100 samples using Quota Sampling to determine the level of customer satisfaction. This study reveals that self-service-based laundry services are supported by optimizing business strategies, especially in digital-based integrated service marketing communications that have been carried out. The results of the study revealed that customers stated quite high levels of satisfaction and loyalty. This research contributes to the service marketing literature by revealing the perspectives of customer satisfaction and loyalty in self-service-based service businesses. There have been many studies conducted to discuss this laundry service business. However, there are no research findings regarding Self-service Optimization. Creative Commons Attribution-ShareAlike 4.0 International License. 204 | Suryawijaya, Utomo, Rahayuningtyas Jurnal Manajemen (Edisi Elektronik)
Modern Synergetic Neural Network for Synthetic Aperture Radar Target Recognition

Feature extraction is an important process for the automatic recognition of synthetic aperture radar targets, but the rising complexity of the recognition network means that the features are abstractly implied in the network parameters and the performances are difficult to attribute. We propose the modern synergetic neural network (MSNN), which transforms the feature extraction process into the prototype self-learning process by the deep fusion of an autoencoder (AE) and a synergetic neural network. We prove that nonlinear AEs (e.g., stacked and convolutional AE) with ReLU activation functions reach the global minimum when their weights can be divided into tuples of M-P inverses. Therefore, MSNN can use the AE training process as a novel and effective nonlinear prototypes self-learning module. In addition, MSNN improves learning efficiency and performance stability by making the codes spontaneously converge to one-hots with the dynamics of Synergetics instead of loss function manipulation. Experiments on the MSTAR dataset show that MSNN achieves state-of-the-art recognition accuracy. The feature visualization results show that the excellent performance of MSNN stems from the prototype learning to capture features that are not covered in the dataset. These representative prototypes ensure the accurate recognition of new samples.
Ordering Due to Quantum Fluctuations in the Frustrated Heisenberg Model

The Heisenberg antiferromagnet with both the nearest neighbor and the next nearest neighbor interactions is studied in two and three dimensions. The spin wave excitation spectrum is obtained based on the general four sublattice mean field ground state in the region where the next nearest neighbor interactions prevail. It is shown that the continuous degeneracy of the mean field ground state is lifted by the zero-point spin wave fluctuations and the “strip” state with collinear spin ordering is stabilized.
Optimal Scheduling of Combined Electric and Heating Considering the Control Process of CHP Unit and Electric Boiler

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme.
Large eddy simulations of thermal convection at high Rayleigh number

SUMMARY With the vastly improved speed and the new shared-memory architecture of the current massively parallel systems, it is now possible to simulate thermal convection at very high Rayleigh (Ra) number lying in the turbulent regime. However, both the dynamics and the interaction with the turbulence may be simultaneously important. Even when only the large scales are of interest, we cannot simply ignore the smallest scales because of the feedback from strong non-linearities everywhere in the £ow. Large eddy simulation (LES), a time-honoured method in engineering £uid mechanics and meteorology, may be the only way to simulate the time-dependent physics in its full complexity, while keeping a reasonable accuracy at the largest scales in highly nonlinear geophysical £uid dynamical £ows, such as mantle convection in the early Earth, convection inside the Jovian moons and the geodynamo. We have tested here a LES model based on the Smagorinsky assumption for 2-D turbulent convection for a ¢nite Prandtl (Pr) number £uid with Pr~1, free-slip boundary conditions and an aspect ratio of 3. The subgrid-scale model is only employed for the temperature equation, where the steepest gradients are developed. The same model can also be used for in¢nite Prandtl number convection. This LES model has been validated by comparison with direct numerical simulation (DNS) for Ra between 10 8 and 10 10 with a grid up to 512|1536 points. Statistical properties of the £ow based on LES are presented for the probability distribution functions (PDF) in space and also the spectra describing the thermal and kinetic energy distributions for Ra up to 10 10 .
I do not want to be perfect: investigating generation Z students' personal brands on social media for job seeking

PurposeThis research aims to explore generation (Gen) Z's personal branding on social media when job seeking.Design/methodology/approachGen Z students, in their final year of university, were interviewed about personal branding, as well as recruiters and career advisors to gain insights into the recruitment process and expectations of online personal brands. Before interviewing, Gen Z students' LinkedIn profiles were examined, and then fed into the interview process.FindingsUsing impression management theory, the findings show that Gen Z perceive online personal brands as a crucial tool to gain more advantage in job markets. A gap was found between desired and perceived selves in Gen Z's online personal brands. Strategies such as effective self-reflection, authentic communication, self-promotion processes, awareness of risks and constantly controlling digital footprints were suggested to build stronger and more coherent personal brands. Gen Z are in favour of a more dynamic, interactive, work-in-process of authentic personal brands.Originality/valueThis research demonstrates the importance of authentically building online personal branding strategies and tactics to bridge the divide between Gen Z's desired and perceived images in personal branding on social media when job seeking.
Nanogold sol plasmon discattering assay for trace carbendazim in tea coupled aptamer with Au3+-glyoxal-carbon dot nanocatalytic reaction

Carbendazim (CBZ) is a broad-spectrum fungicide, which is toxic to mammals. Therefore, it is very necessary to establish a sensitive detection for food safety. An experiment found that CDFe exhibited excellent catalysis for the nano-indicator reaction of HAuCl4-glyoxal to produce gold nanoparticles (AuNPs) and that the generated AuNPs have a very strong surface-enhanced Raman scattering (SERS) effect at 1613 cm−1 in the presence of Victoria blue B molecular probes, and resonance Rayleigh scattering (RRS) signals at 370 nm. The aptamer (Apt) suppressed the catalysis of CDFe to cause the SERS and RRS signals decreasing. With the addition of CBZ, the specific Apt reaction occurred to restore the catalysis of CDFe, and resulting in a linear increase in the signals of RRS and SERS. As a result, this new nanocatalytic amplification indicator reaction was coupled with a specific Apt reaction of carbendazim (CBZ), to construct a new CDFe catalytic amplification-aptamer SERS/RRS discattering assay for ultratrace CBZ, which was used to analyze CBZ in tea samples with satisfactory results. In addition, this biosensoring platform can be also used to assay profenofos.
Analysis of the therapeutic interaction provided by a humanoid robot serving stroke survivors as a therapeutic assistant for arm rehabilitation

Objective: To characterize a socially active humanoid robot’s therapeutic interaction as a therapeutic assistant when providing arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis or arm ability training (AAT) for mild arm paresis) to stroke survivors when using the digital therapeutic system Evidence-Based Robot-Assistant in Neurorehabilitation (E-BRAiN) and to compare it to human therapists’ interaction. Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., ABT [n = 9] or AAT [n = 8]) using E-BRAiN over a course of nine sessions and twenty-one other stroke survivors receiving arm rehabilitation sessions (i.e., ABT [n = 6] or AAT [n = 15]) in a conventional 1:1 therapist–patient setting. Analysis of therapeutic interaction: Therapy sessions were videotaped, and all therapeutic interactions (information provision, feedback, and bond-related interaction) were documented offline both in terms of their frequency of occurrence and time used for the respective type of interaction using the instrument THER-I-ACT. Statistical analyses: The therapeutic interaction of the humanoid robot, supervising staff/therapists, and helpers on day 1 is reported as mean across subjects for each type of therapy (i.e., ABT and AAT) as descriptive statistics. Effects of time (day 1 vs. day 9) on the humanoid robot interaction were analyzed by repeated-measures analysis of variance (rmANOVA) together with the between-subject factor type of therapy (ABT vs. AAT). The between-subject effect of the agent (humanoid robot vs. human therapist; day 1) was analyzed together with the factor therapy (ABT vs. AAT) by ANOVA. Main results and interpretation: The overall pattern of the therapeutic interaction by the humanoid robot was comprehensive and varied considerably with the type of therapy (as clinically indicated and intended), largely comparable to human therapists’ interaction, and adapted according to needs for interaction over time. Even substantially long robot-assisted therapy sessions seemed acceptable to stroke survivors and promoted engaged patients’ training behavior. Conclusion: Humanoid robot interaction as implemented in the digital system E-BRAiN matches the human therapeutic interaction and its modification across therapies well and promotes engaged training behavior by patients. These characteristics support its clinical use as a therapeutic assistant and, hence, its application to support specific and intensive restorative training for stroke survivors.
Bibliometric Studies Perspective on Child Protection Policy

For this inquiry, papers from international journals that are indexed by SCOPUS are accessed using the VosViewer 1.6. 17 software. Nonetheless, 533 articles from the years 1975 to 2021 were picked with the keywords ”CHILD PROTECTION, CHID ABUSE, AND CHILD POLICY. ” Scholarly interest in this subject increased significantly between the years 2017 and 2021; the highest year was 2018 when 50 publications on the subject of child protection were published. In general, the findings of bibliometric study on ”CHILD PROTECTION, CHILD ABUSE, AND CHILD POLICY,” especially forms of violence and the causes of violence against children, are currently being researched in the field of psychology. Child safety and public policy are still hotly debated subjects. So far, there are just 18 articles. Keywords: child protection, child abuse, child policy, bibliometrics
Statistical estimation of polynomial generalized covariance functions and hydrologic applications

Minimum-variance unbiased linear estimation theory has found many applications in the analysis of spatial rainfall and Hydrogeologic data: estimation of point values or areal averages, calculation of variances of estimation error, and network design. However, the important problem of inferring the spatial structure or interdependence of the function of interest has received relatively less attention. Three methods for the estimation of the parameters of Matheron's polynomial generalized covariance function (a useful model for nonstationary functions) are presented and evaluated: Maximum likelihood (ML), minimum variance unbiased quadratic (MVUQ), and minimum norm (least squares). The estimation error covariance matrix of the parameters is calculated with each of these methods. ML and MVUQ estimation have very good properties and, for reasonably large samples, give very similar results. Minimum norm estimation is appropriate for preliminary data analysis. The application of these methodologies is illustrated with a set of rainfall data, and problems of implementation are discussed. It is shown that the estimation of the parameters of the covariance function involves sizeable errors. It is suggested that, in practice, it may be rather difficult to identify the parameters of all but simple models. It is important that future research evaluate the effects of parameter uncertainty in the circulation of estimation error variances of point or areal averages and in the design of hydrologic data collection networks.
Evaluation Machine-Learning Approaches for Classification of Cryotherapy and Immunotherapy Datasets

—Machine-learning (ML) methods have great importance when applied interdisciplinary. Besides many areas, ML methods save cost and time in medical applications. In this study, we experimented several ML methods with different approaches on classification of Cryotherapy and Immunotherapy datasets, which are applied on wart treatment. The effects of dimension reduction techniques and handling of unbalanced sample classes are the main discussion points of our study. When several ML models are analyzed, Random Forest (RF) achieved 95% accuracy, %88 sensitivity, and %98 specificity. Other ML methods also performed successful results close to the RF. Although some promising results were obtained, we also discussed the drawbacks of these approaches while evaluating wart treatment strategies.
PoE: Poisoning Enhancement Through Label Smoothing in Federated Learning

In federated learning (FL), poisoning attack invades the whole system by manipulating the client data, tampering with the training target, and performing any desired behaviors. Until now, numerous poisoning attacks have been carefully studied, however they are still practically challenged in real-world scenarios from two aspects: (i) <italic>multiple malicious client selections</italic>- poisoning attacks are only successfully launched when the malicious client has been chosen in enough epochs (i.e., more than half of the epochs); (ii) <italic>long-term poisoning training</italic>- the poisoning training usually needs much more epochs than the normal training (i.e., 3 times longer than normal training), both are unavailable in real cases. To address these overlooked problems, we propose a <italic>Po</italic> isoning <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>nhanced attack (PoE) against FLs, which is a general poisoning reinforcement framework. It is designed to transfer partially predicted probabilities of the source class to the target one. Thus, the inter-class distance between the source class and the target class is narrowed down in feature space for easier attacks. Towards this goal, the attack client uses label smoothing to change the model prediction distribution, dragging the global model in the direction that is favorable for poisoning. Extensive experiments show that PoE can significantly enhance the attack success rate (<inline-formula> <tex-math notation="LaTeX">$\sim \times8.4$ </tex-math></inline-formula> on average) in practical FLs with normal training epochs. It also achieves state-of-the-art adaptive attack performance against defensive FLs (i.e., robust aggregations). The code of PoE could be downloaded at <uri>https://github.com/Leon022/poisoning_enhancement</uri>.
Matching the pitch of a mistuned harmonic in an otherwise periodic complex tone

Listeners adjusted the frequency of a sine tone to match the pitch of a single mistuned harmonic in a complex tone consisting of 16 harmonics. Fundamental frequencies were 200, 400, or 800 Hz, and mistunings ranged from 0.5% to 8.0%. The successful matches, those close to the actual mistuned harmonic, show highly consistent and unexpected pitch shift effects; the pitch shifts can be twice as large as the frequency shifts, for both positive and negative frequency shifts. The percentage of matches which are successful provides an estimate of a listener's ability to segregate the mistuned component from the complex tone. Best performance occurs for mistuned harmonics in the region of spectral dominance. The highest frequency which can be segregated is a decreasing function of the fundamental frequency. The latter result suggests that the segregation operation depends upon both place and time coding in the auditory system. [Work supported by the NIH, the NSF, and the CHRS—France. ]
Gb-Dbscan: A Fast Granular-Ball Based Dbscan Clustering Algorithm

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) iden-tiﬁes high-density connected areas as clusters, so that it has advantages in discovering arbitrary-shaped clusters. However, it has diﬃculty in adjusting parameters and since it needs to scan all data points in turn, its time complexity is O ( n 2 ). Granular-ball (GB) is a multi-granularity data representation. It is on the basis of the fact that an object and its local neighbors have similar distribution, so they have high possibility of belonging to the same class. In this paper, we introduce it into unsupervised learning and use it to improve the eﬃciency of DBSCAN. Firstly, we use k-nearest neighbor (KNN) to generate granular-balls, which is a bottom-up strategy, and describe granular-balls according to their centers and radius. Then, the granular-balls are divided into Core-GB and Non-Core-GB according to their density. After that, we deﬁne density reachable on GBs to merge the Core-GBs and allocate the Non-Core-GBs to the appropriate clusters. Since the GBs’ number is much smaller than the size of the objects in a data, the running time of DBSCAN is greatly reduced. By comparing with some stat-of-the-art algorithms, such as KNN-BLOCK DBSCAN, RNN-DBSCAN, DBSCAN, K-means, DP and SNN-DPC algorithms, the proposed algorithm can get similar or better clustering result in much less running time.
Isolation of Intact Vacuoles from Arabidopsis Root Protoplasts and Elemental Analysis

The vacuole is one of the most conspicuous organelles in plant cells, participating in a series of physiological processes, such as storage of ions and compartmentalization of heavy metals. Isolation of intact vacuoles and elemental analysis provides a powerful method to investigate the functions and regulatory mechanisms of tonoplast transporters. Here, we present a protocol to isolate intact vacuoles from Arabidopsis root protoplasts and analyze their elemental content by inductively coupled plasma mass spectrometry (ICP-MS). In this protocol, we summarize how to prepare the protoplast, extract the vacuole, and analyze element concentration. This protocol has been applied to explore the function and regulatory mechanisms of tonoplast manganese (Mn) transporter MTP8, which is antagonistically regulated by CPK4/5/6/11 and CBL2/3-CIPK3/9/26. This protocol is not only suitable for exploring the functions and regulatory mechanisms of tonoplast transporters, but also for researching other tonoplast proteins. Graphical abstract
Slurry Leakage Channel Detection and Slurry Transport Process Simulation for Overburden Bed Separation Grouting Project: A Case Study from the Wuyang Coal Mine, Northern China

The 8006 working face at the Wuyang Coal Mine adopts grout injection into bed separation technology for surface subsidence control. Surface grout leakage occurred during the grout injection into the bed separation process of this working face. Grout leakage has adverse effects on the grouting filling effect, grouting cost and the environment. To determine the grout leakage channels and the slurry transport process, and to provide a theoretical basis for slurry leakage prevention and control, this paper first used 3D seismic exploration technology to identify the fault distribution characteristics of the study area, and then used COMSOL Multiphysics to carry out the numerical simulation of the grout transport process. The conclusions are as follows. Fifteen normal faults were identified in the vicinity of the 8006 working face. Among all the faults, the F1, F11, F18, F19 and F27 faults penetrate the surface and are the main channels for the grout to run to the surface. Based on the distribution characteristics of the faults and the spatial location relationship among the bed separation, faults and grout leakage points, the theoretical analysis of the leakage causes of each grout leakage point was carried out, and the main leakage channels of the grout injection into bed separation were proposed to be the bed separation and faults. The results of the numerical simulation of grout transport show that, as the permeability of the bed separation space and fault is much better than that of the surrounding rock, during the grout injection process the grout diffuses through the bed separation and fault in turn, and finally to the surface, where leakage occurs. The simulation results confirm that the main leakage channels for the grout are bed separation and faults.
ALMOST INTEGRABLE MAPPINGS

We analyze birational transformations obtained from very simple algebraic calculations, namely taking the inverse of q × q matrices and permuting some of the entries of these matrices. We concentrate on 4 × 4 matrices and elementary transpositions of two entries. This analysis brings out six classes of birational transformations. Three classes correspond to integrable mappings, their iteration yielding elliptic curves. Generically, the iterations corresponding to the three other classes are included in higher dimensional non-trivial algebraic varieties. Nevertheless some orbits of the parameter space lie on (transcendental) curves. These transformations act on fifteen (or q2 − 1) variables, however one can associate to them remarkably simple non-linear recurrences bearing on a single variable. The study of these last recurrences gives a complementary understanding of these amazingly regular non-integrable mappings, which could provide interesting tools to analyze weak chaos.