Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Drought and Changing Water Sources on Water Use and Soil Salinity of Almond and Pistachio Orchards: 2. Modeling

California is increasingly experiencing drought conditions that restrict irrigation deliveries to perennial nut crops such as almonds and pistachios. During drought, poorer quality groundwater is often used to maintain these crops, but this use often results in secondary salinization that requires skilled management. Process-based models can help improve management guidelines under these challenging circumstances. The main objective of this work was to assess seasonal soil salinity and root water uptake as a function of irrigation water salinity and annual rain amounts. The manuscript presents a comparison of three-year experimental and numerically simulated root zone salinities in and below the root zone of almond and pistachio drip-irrigated orchards at multiple locations in the San Joaquin Valley (SJV), California, with different meteorological characteristics. The HYDRUS-1D numerical model was calibrated and validated using field measurements of soil water contents and soil solute bulk electrical conductivities at four root zone depths and measured soil hydraulic conductivities. The remaining soil hydraulic parameters were estimated inversely. Observations and simulations showed that the effects of rain on root zone salinity were higher in fields with initially low salinities than in fields with high salinities. The maximum reduction in simulated root water uptake (7%) occurred in response to initially high soil salinity conditions and saline irrigation water. The minimum reduction in simulated water uptake (2.5%) occurred in response to initially low soil salinity conditions and a wet rain year. Simulated water uptake reductions and leaching fractions varied at early and late times of the growing season, depending on irrigation water salinity. Root water uptake reduction was highly correlated with the cumulative effects of using saline waters in prior years, more than salt leaching during a particular season, even when rain was sufficient to leach salts during a wet year.
FACTORS AFFECTING ONLINE PURCHASE INTENTION

Pemasaran merupakan kegiatan yang harus dilakukan oleh perusahaan dalam mempertahankan keberlangsungan bisnisnya. Banyak strategi yang dapat dilakukan oleh perusahaan dalam memasarkan produknya agar dapat dikenali dan diyakini dapat memenuhi kebutuhan konsumennya. Dalam menentukan strategi pemasaran tentunya perusahaan harus mengetahui terlebih dahulu faktor apa yang paling mempengaruhi dalam keputusan pembelian konsumen. Ketika perusahaan sudah mengetahui faktor yang paling mempengaruhi keputusan pembelian maka hal tersebut akan memudahkan perusahaan untuk menentukan strategi pemasaran. Penelitian ini bermaksud untuk mengetahui faktor yang mempengaruhi niat pembelian online khususnya yang terjadi pada e-commerce di Indonesia. Penelitian ini fokus pada tujuh faktor pengaruh pembelian yaitu kepercayaan, kompetensi bisnis, referensi grup, persepsi risiko, kemudahan dan kenyamanan, kesadaran kontrol perilaku dan promosi. Data diperoleh dari 274 responden dengan menggunakan teknik Purposive Sampling melalui kuesioner yang didistribusikan melalui media sosial dengan menggunakan Google Form. Data hasil kuesioner dianalisis menggunakan metode regresi menunjukkan bahwa pengaruh faktor yang mempengaruhi sesuai dengan urutan yang paling mempengaruhi adalah promosi, kompetensi bisnis, kemudahan dan kenyamanan, referensi kelompok, dan kepercayaan.
Incremental Integration: A Successful Service-Learning Strategy

Many colleges and universities have embraced service learning, but the enthusiasm of administrators often exceeds service-learning application at the classroom level. For a variety of reasons, educators hesitate to implement service learning in their courses. Understanding service learning as a pedagogical tool is the key. Both students and faculty need time to learn the strategies and practices of service learning in order to succeed. This paper discusses strategies for integrating service learning into a department-wide curriculum, using a sequence of horticulture courses as an example. By increasing the service-learning component with each successive course, teachers and students gain confidence in the method and therefore are more likely to have positive results in courses with a greater service-learning component.
INFERENCE ON SURVIVAL DATA UNDER NONPROPORTIONAL HAZARDS

The objective of this research is to develop optimal (efficient) test methods for analysis of survival data under random censorship with nonproportional hazards. For the first part we revisit the weighted log-rank test where the weight function was derived by assuming the inverse Gaussian distribution for an omitted exponentiated covariate that induces a nonproportionality under the proportional hazards model. We perform a simulation study to compare the new procedure with ones using other popular weight functions including members of the Harrington-Fleming's G-rho family. The nonproportional hazards data are generated by changing the hazard ratios over time under the proportional hazards model. The results indicate that the inverse Gaussian-based test tends to have higher power than some of the members that belong to the G-rho family in detecting a difference between two survival distributions when populations become homogeneous as time progresses. The second part of the research includes development of a parametric method in detecting the validity of the proportional odds model assumption between two groups of survival data. The research is based on the premise that the test procedure developed would take advantage of knowledge of the distributional information about the data, which will improve the sensitivity of a nonparametric test method. We evaluate type I error and power probabilities of the new parametric test by using the simulated survival data following the log-logistic distribution. The error probabilities are compared with ones in the literature. The results indicate that the extended test performs with a higher sensitivity than the existing nonparametric method.The results from the proposed study provide statistical test methods that are more sensitive than existing ones under certain situations which can be used in public health relevance applications such as clinical trials.
Augmented Reality in Physical Therapy: Systematic Review and Meta-analysis

Background Augmented reality (AR) is a rapidly expanding technology; it comprises the generation of new images from digital information in the real physical environment of a person, which simulates an environment where the artificial and real are mixed. The use of AR in physiotherapy has shown benefits in certain areas of patient health. However, these benefits have not been studied as a whole. Objective This study aims to ascertain the current scientific evidence on AR therapy as a complement to physiotherapy and to determine the areas in which it has been used the most and which variables and methods have been most effective. Methods A systematic review registered in PROSPERO (International Prospective Register of Systematic Reviews) was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) recommendations. The search was conducted from July to August 2021 in the PubMed, PEDro, Web of Science, Scopus, and Cochrane Library scientific databases using the keywords augmented reality, physiotherapy, physical therapy, exercise therapy, rehabilitation, physical medicine, fitness, and occupational therapy. The methodological quality was evaluated using the PEDro scale and the Scottish Intercollegiate Guidelines Network scale to determine the degree of recommendation. The Cochrane Collaboration tool was used to evaluate the risk of bias. Results In total, 11 articles were included in the systematic review. Of the 11 articles, 4 (36%) contributed information to the meta-analysis. Overall, 64% (7/11) obtained a good level of evidence, and most had a B degree of recommendation of evidence. A total of 308 participants were analyzed. Favorable results were found for the Berg Balance Scale (standardized mean change 0.473, 95% CI −0.0877 to 1.0338; z=1.65; P=.10) and the Timed Up and Go test (standardized mean change −1.211, 95% CI −3.2005 to 0.7768; z=−1.194; P=.23). Conclusions AR, in combination with conventional therapy, has been used for the treatment of balance and fall prevention in geriatrics, lower and upper limb functionality in stroke, pain in phantom pain syndrome, and turning in place in patients with Parkinson disease with freezing of gait. AR is effective for the improvement of balance; however, given the small size of the samples and the high heterogeneity of the studies, the results were not conclusive. Future studies using larger sample sizes and with greater homogeneity in terms of the devices used and the frequency and intensity of the interventions are needed. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020180766; https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=180766
POLÍTICAS DE INCLUSÃO NA PÓS-GRADUAÇÃO: Os bastidores e o histórico da edição da portaria normativa 13/2016

RESUMO O artigo analisa como as acoes afirmativas na pos-graduacao ingressaram na agenda decisoria da Capes e do MEC, bem como o processo que resultou na Portaria Normativa n. 13/2016. Baseia-se em documentos e em entrevistas com participes do grupo de trabalho que discutiu o tema e representantes da Capes e do MEC. O trabalho mostra que a questao nao alcancou a agenda geral de politicas publicas do pais e que ha resistencia a edicao de normas vinculantes.
Modelling the dynamic response of the human spine to shock and vibration using a recurrent neural network

The ability to model the spine's response to mechanical shock and vibration is an important step in assessing the health hazards of repeated impacts to vehicle passengers. Current linear models, such as the Dynamic Response Index (DRI) and the British Standard 6841 filter (BS 6841), perform poorly when the input consists of large–magnitude shocks typical of those experienced by personnel in military vehicles. In this study, a recurrent neural network (RNN) was developed which models the spinal acceleration response of the seated passenger at the LA vertebra to vertical accelerations applied at the seat. ARNN is a universal nonlinear approximator that can, in theory, model any system with memory if trained with a representative set of measured input–output data. The seat–spine system was modelled as a network with four inputs and one output. The back propagation algorithm was used to train the network by adjusting network parameters to minimise the square of the prediction error. The inputs to the network were delayed values of the inputs and outputs. The trained network significantly outperformed the two linear models examined for predicting the z–axis acceleration at the L4 vertebra.
Automatic structure detection in a point-cloud of buildings obtained by terrestrial laser scanning

In recent years, terrestrial laser scanner (TLS) has become a popular data acquisition tool for producing irregularly-spaced point clouds as well as airborne laser scanning (ALS). Automated detection of structures (roof and ground etc.) based on the point cloud analysis of buildings has become increasingly important. One of the most demanding tasks in TLS is the filtering of the ground and roofs in TLS point clouds. This paper proposes a method for detecting buildings' structures from an irregularly-spaced point-cloud. This method is consisted of segmentation and classification. As the previously developed the segmentation methods can not be applied to it directly, it has to perform twice pre-filtration so as to proceed to further calculation for segmentation and classification. More importantly the algorithm is extensible and future work will further strengthen the algorithm.
MVO-Autism: An Effective Pre-treatment with High Performance for Improving Diagnosis of Autism Mellitus

Background and Objectives: Autism is the most well-known disease that occurs in any age people. There is an increasing concern in appealing machine learning techniques to diagnose these incurable conditions. But, the poor quality of most datasets contains the production of efficient models for the forecast of autism. The lack of suitable pre-processing methods outlines inaccurate and unstable results. For diagnosing the disease, the techniques handled to improve the classification performance yielded better results, and other computerized technologies were applied.Methods: An effective and high performance model was introduced to address pre-processing problems such as missing values and outliers. Several based classifiers applied on a well-known autism data set in the classification stage. Among many alternatives, we remarked that combine replacement with the mean and improvement selection with Random Forest and Decision Tree technologies provide our obtained highest results.Results: The best-obtained accuracy, precision, recall, and F-Measure values of the MVO-Autism suggested model were the same, and equal 100% outperforms their counterparts. Conclusion: The obtained results reveal that the suggested model can increase classification performance in terms of evaluation metrics. The results are evidence that the MVO-Autism model outperforms its counterparts. The reason is that this model overcomes both problems.
Multigigabit CMOS limiting amplifier and VCSEL driver arrays for parallel optical interconnects

Limiting amplifier and VCSEL driver arrays for parallel optical interconnects operating at 8 Gb/s are presented, which are designed and fabricated in a Si‐based 0.18‐μm CMOS technology for the single chip integration with low‐power consumption. The limiting amplifier array showed a differential gain of 26 dB, a −3‐dB bandwidth of 6.8 GHz, and a crosstalk of −32 dB between channels. For the VCSEL driver array, it showed a differential gain of 25 dB, a −3‐dB bandwidth of 6.3 GHz, and crosstalk of −26 dB between channels. The power dissipation per channel of the limiting amplifier and the VCSEL driver are 45 and 120 mW, respectively. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1656–1659, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21691
Investigating the Personalization–Privacy Paradox in Internet of Things (IoT) Based on Dual-Factor Theory: Moderating Effects of Type of IoT Service and User Value

Despite people’s concerns over privacy leakage in the Internet of Things (IoT), the needs for personalized IoT services are increasing, creating a conflicting phenomenon viewed as the personalization–privacy (P–P) paradox. This study proposes a research model that utilizes dual-factor theory to investigate the P–P paradox in IoT. It aims to analyze the impact of the dual factor—personalization and privacy concerns related to IoT services—on the intention to use IoT. Further, the model includes four-dimensional motivated innovativeness and previous privacy-invasion experience as key antecedents of the dual factor. Particularly, this study examines the moderating effects of the type of IoT service and user value on the relationship between dual factor and usage intention. Data were collected using a web-based survey. The results showed that personalization had a significant impact on the intention to use IoT, whereas privacy concerns did not. The effects of all antecedents except social innovativeness were significant. The P–P paradox phenomenon appeared differently depending on the type of IoT service and user value. This study contributes to gaining a better understanding of the factors that influence the increase in IoT usage in terms of both protecting and appropriately using personal information for IoT services.
Investigating the effectiveness of self-management program based on 5A model on fatigue and dyspnea in patients with heart failure.

BACKGROUND Some symptoms such as fatigue and dyspnea decrease the quality of life in patients with heart failure. The effectiveness of self-management programs on management of chronic conditions was discussed. So, this study aimed to investigate the effectiveness of self-management program based on 5A model on fatigue and dyspnea in patients with heart failure. METHODS In this clinical trial study, 60 patients with heart failure were included. The intervention group underwent self-management program based on 5A model and the control group received routine care. All the enrolled patients were evaluated once at baseline and once after 3 months using fatigue severity and Borg dyspnea scales. The obtained data were analyzed using SPSS software version 16 by descriptive statistics and independent t-test, covariance test, and paired sample t- test. The significance level was set at 0.05. RESULTS There was no significant difference in the mean scores of fatigue and dyspnea at the beginning of the study between control and intervention groups (p > 0.05), but 3 months after intervention a significant difference was found in mean scores of fatigue and dyspnea between two groups, (p < 0.05). The difference between pre- and post-intervention scores in terms of the dyspnea and fatigue variables was significant based on the result of paired sample t-test (p < 0.05). CONCLUSION According to the results of this study, self-management program based on 5A Model can be used to reduce the severity of fatigue and dyspnea as well as improve the quality of life in patients with heart failure (Tab. 4, Fig. 1, Ref. 33).
Learning from Failures: Using Historical Engineering Projects to Teach Better Professional Engineering Skills

The state of the art of engineering knowledge has historically often improved following reviews of major disasters and engineering failures. It is not desirable however for professional engineers to only improve their understanding and skills by “learning from their mistakes”. A new final-year engineering course for Civil and Natural Resources Engineering students at the University of Canterbury aims to get students to learn more from other people’s past mistakes. A major component of this course is a group project where students investigate notable engineering “failures” from the past century and try to determine the causes behind them. As well as any direct technical reasons for each failure, students are challenged to identify the more “nontechnical” issues that contributed to the ultimate denouement, including human errors, ethical shortcomings, and regulatory omissions. Using this exercise, it is hoped that students will learn to recognise common “warning signs” in their future projects that may be pre-cursors to more catastrophic potential outcomes.
Transport Mechanism of Water and Sediment in Yangtze River Estuary North-South Branch Bifurcation

This Base on the the field data of water and sediment of Yangtze River estuary bifurcation in 2004 fiood season and 2005 dry season, the features and process of water and sediment transport Was analyzed by decomposition mechanism.The law of velocity of residual currentsediment transport rate per unit width and tidal punping effect,which in different season and tide,are focused on.The relationship between the features of water and sediment and channel wvolution was discussed through the results of the discharge of water and sediment,which is a basis in the future study of discharge of water and sediment in estuary.
Identi�cation of MicroRNA-mRNA Regulatory Networks in Periodontitis by Bioinformatics Analysis

Background: Periodontitis is a complex infectious disease with various causes and contributing factors. In recent years, microRNAs (miRNAs) have been commonly accepted as having key regulatory functions in periodontal disease. The aim of this study was to identify miRNAs and hub genes involved in periodontal disease pathogenesis using a miRNA-mRNA interaction network. Methods: The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using P <0.05 and |log FC| ≥ 1. Potential upstream transcription factors and downstream target genes of candidate DEMis were predicted using the FunRich and miRNet programs, respectively. Subsequently, DEMs were uploaded to the STRING database, a protein-protein interaction (PPI) network was established, and the cytoHubba plugin was used to screen out key hub mRNAs. The key genes in the miRNA-mRNA regulatory network were extracted by intersecting the target genes of candidate DEMis and DEMs. Cytoscape software was used to visualise the interaction between miRNAs and mRNAs and to predict the hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyse the key genes in the regulatory network. Results: Ten DEMis and 161 DEMs were �ltered out, from which we constructed a miRNA-mRNA network consisting of six miRNAs and 32 mRNAs. KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. Hsa-miR-203/CXCL8, hsa-miR-203/BTG2, and hsa-miR-203/DNAJB9 were identi�ed as four potential regulatory pathways for periodontitis. Conclusion: In this study, a potential miRNA–mRNA regulatory network was �rst constructed and four regulatory pathways were identi�ed for periodontitis to help clarify the aetiology of the disease and provide potential therapeutic targets.
An Exploratory Study of the Relationship Between Financial Well-Being and Changes in Reported Gambling Behaviour During the COVID-19 Shutdown in Australia

A change in someone’s financial situation, such as a windfall gain or increased financial stress, can affect the way that they gamble. The aim of this paper was to explore the relationship between financial well-being and changes in gambling behaviour during the coronavirus 2019 (COVID-19) shutdown. Australian past-year gamblers (N = 764; 85% male) completed an online cross-sectional survey in May 2020. Participants retrospectively reported monthly gambling participation before and after the COVID-19 shutdown, as well as their financial well-being, experience of COVID-related financial hardship, problem gambling severity, and psychological distress. Financial well-being showed strong negative associations with problem gambling and psychological distress. Neither financial well-being nor the interaction between financial well-being and problem gambling severity showed consistent evidence for predicting changes in gambling participation during the shutdown in this sample. This study provides preliminary evidence that self-reported financial well-being has a strong negative association with gambling problems but is not related to gambling participation. Future studies should link objective measures of financial well-being from bank transaction data with survey measures of problem gambling severity and experience of gambling-related harm.