Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGELOLAAN CUSTOMER ENGAGEMENT PADA MEDIA SOSIAL UNTUK MEMBIDIK PASAR MILENIAL PADA TAHUN 2019 (Studi Kasus Pengelolaan Konten Promosi Instagram Labalaba Periode September 2018 – Maret 2019)

Pengelolaan customer engagement menjadi proses yang sangat penting dalam melakukan pemasaran secara online. Labalaba merupakan market leader usaha yang bergerak dalam bidang jasa reparasi barang selama lebih dari 100 tahun. Labalaba berupaya untuk mulai mengelola customer engagement pada pasar baru yakni generasi Milenial. Penelitian ini bertujuan untuk mengetahui bagaimana penggunaan konten sosial media Instagram dalam mengelola customer engagement pada suatu brand dengan pelanggan generasi Milenial. Teori yang digunakan adalah teori Sashi (2012) sebuah brand dapat menggunakan social media sebagai alat untuk mengelola customer engagement. Sehingga ada hubungan antara pengelolaan social media dan customer engagement. Penelitian ini menggunakan paradigma Kontruktivisme, pendekatan kualitatif dan bersifat studi kasus. Pengumpulan data yang dilakukan dengan teknik data primer dan sekunder. Data primer didapatkan dari hasil wawancara oleh beberapa informan dan data sekunder didapatkan dari arsip dan data yang dimiliki perusahaan, seperti website, dan laporan akun Instagram @labalabaid. Hasil dari penelitian ini menunjukan bahwa dalam pengelolaan customer engagement pada sosial media Instagram khususnya untuk membidik Generasi Milenial, perlu ada pengelolaan dari insight-insight yang dibangun dari audience itu sendiri. Dari insight itulah, sebuah brand dapat membuat konten yang membangun engagement dengan audiencenya. Teknik konten promosi yang dipilih oleh Labalaba adalah softselling. Yang dimana lebih mengutamakan value dari produk atau jasa yang ditawarkan.
The Role of Preoperative Educational Intervention in Reducing Parental Anxiety

Background and objective The parents of pediatric patients admitted for elective surgery exhibit significant levels of anxiety. The reduction in parental anxiety is directly proportional to the information and counseling provided to the parents preoperatively. The parenting style in South Asian culture is different from that of western cultures and may influence the response to these interventions. In this study, we aimed to compare the mean anxiety levels between parents of children (aged three to eight years) undergoing outpatient infraumbilical surgery equipped with standardized preoperative parental education and those without. Methods This was a randomized, controlled, and blinded trial. Parents of 72 pediatric patients (aged three to eight years) undergoing elective infraumbilical daycare surgery were enrolled and were divided into two groups: an intervention (Group I) and a non-intervention (Group NI) group. Both groups received routine verbal counseling at the preoperative clinic, but a standardized brochure was provided only to Group I. Parental anxiety was measured by using the Visual Analog Scale (VAS) at three different time points: in the outpatient surgery suite on the day of surgery, in the preoperative area 10 minutes before shifting the child to the operating room, and finally in the recovery room. Results The baseline mean VAS score was significantly higher in Group I compared to Group NI (p=0.017). After the intervention, the mean pain score significantly decreased from baseline in Group I as compared to Group NI (mean ±SD: 4.08 ±1.6 vs. 6.08 ±1.66; p=0.0005). Conclusion The information provided through standardized written material to the parents on the day of surgery before anesthesia helped to significantly attenuate preoperative parental anxiety.
Note on Transformation-Optics-Based Flat Metamaterial Luneburg Lens Antenna with Zero-Focal Plane

This paper discusses the fundamental issues of the application of transformation-optics (TO) technique to the volume reduction of Luneburg lens (LL). The issue how to keep the invariance of Maxwell’s equation after TO implementation is addressed. The invariance is the key to keep the focal plane of lens on or near the lens surface after the transformation. Well known, the focus location shift away from the lens surface results in a limited volume reduction, severe reflection from the transformed lens surface, and limited scanning range with increased spillover loss. After theoretical study, two designs based on the TO are exemplified to show how to realize zero-focal or near-surface focal LL antennas. One flat LL antenna is first introduced to achieve the volume reduction of whole antenna by 2/3 using three-dimensional printing metamaterials. Then a hemispherical LL antenna is presented to show the wide beamscanning range and easy fabrication using multi-layered metamaterials when a near-surface focal plane is achieved.
Finding Nontrivial Minimum Fixed Points in Discrete Dynamical Systems: Complexity, Special Case Algorithms and Heuristics

Networked discrete dynamical systems are often used to model the spread of contagions and decision-making by agents in coordination games. Fixed points of such dynamical systems represent configurations to which the system converges. In the dissemination of undesirable contagions (such as rumors and misinformation), convergence to fixed points with a small number of affected nodes is a desirable goal. Motivated by such considerations, we formulate a novel optimization problem of finding a nontrivial fixed point of the system with the minimum number of affected nodes. We establish that, unless P = NP, there is no polynomial-time algorithm for approximating a solution to this problem to within the factor n^(1 - epsilon) for any constant epsilon > 0. To cope with this computational intractability, we identify several special cases for which the problem can be solved efficiently. Further, we introduce an integer linear program to address the problem for networks of reasonable sizes. For solving the problem on larger networks, we propose a general heuristic framework along with greedy selection methods. Extensive experimental results on real-world networks demonstrate the effectiveness of the proposed heuristics. A full version of the manuscript, source code and data are available at: https://github.com/bridgelessqiu/NMIN-FPE
Importance of liver biopsy findings in immunosuppression management: Biopsy monitoring and working criteria for patients with operational tolerance

Obstacles to morbidity‐free long‐term survival after liver transplantation (LT) include complications of immunosuppression (IS), recurrence of the original disease and malignancies, and unexplained chronic hepatitis and graft fibrosis. Many programs attempt to minimize chronic exposure to IS by reducing dosages and stopping steroids. A few programs have successfully weaned a highly select group of recipients from all IS without apparent adverse consequences, but long‐term follow‐up is limited. Patients subjected to adjustments in IS are usually followed by serial liver chemistry tests, which are relatively insensitive methods for detecting allograft damage. Protocol biopsy has largely been abandoned for hepatitis C virus–negative recipients, at least in part because of the inability to integrate routine histopathological findings into a rational clinical management algorithm. Recognizing a need to more precisely categorize and determine the clinical significance of findings in long‐term biopsy samples, the Banff Working Group on Liver Allograft Pathology has reviewed the literature, pooled the experience of its members, and proposed working definitions for biopsy changes that (1) are conducive to lowering IS and are compatible with operational tolerance (OT) and (2) raise concern for closer follow‐up and perhaps increased IS during or after IS weaning. The establishment of guidelines should help us to standardize analyses of the effects of various treatments and/or weaning protocols and more rigorously categorize patients who are assumed to show OT. Long‐term follow‐up using standardized criteria will help us to determine the consequences of lowering IS and to define and determine the incidence and robustness of OT in liver allografts. Liver Transpl 18:1154–1170, 2012. © 2012 AASLD.
NT-proBNP and Heart Failure Risk Among Individuals With and Without Obesity: The ARIC Study

Background —Obesity is a risk factor for heart failure (HF), but is associated with lower N-terminal of pro-Brain Natriuretic Peptide (NT-proBNP) levels. It is unclear whether the prognostic value and implications of NT-proBNP levels for HF risk differ across body mass index (BMI) categories. Methods and Results —We followed 12,230 ARIC participants free of prior HF at baseline (visit 2, 1990-1992) with BMI ≥18.5 kg/m2. We quantified and compared the relative and absolute risk associations of NT-proBNP with incident HF across BMI categories. There were 1,861 HF events during a median 20.6 years of follow-up. Despite increased HF risk in obesity, a weak inverse association was seen between baseline BMI and NT-proBNP levels (r = -0.10). Nevertheless, higher baseline NT-proBNP was associated with increased HF risk in all BMI categories. NT-proBNP improved HF risk prediction overall and even among those with severe obesity (BMI ≥35 kg/m2; improvement in c-statistic +0.032 [95% CI 0.011-0.053]). However, given higher HF rates among those with obesity, at each NT-proBNP level, higher BMI was associated with greater absolute HF risk. Indeed, among those with NT-proBNP 100 to 10% if severely obese. Conclusions —Despite its inverse relationship with BMI, NT-proBNP provides significant prognostic information regarding the risk of developing HF even among individuals with obesity. Given the higher baseline HF risk among persons with obesity, even slight elevations in NT-proBNP may have implications for increased absolute HF risk in this population.
Preoperative thoracic radiographic findings in dogs presenting for gastric dilatation-volvulus (2000–2010): 101 cases

Objective To identify the incidence of clinically significant findings on preoperative thoracic radiographs in dogs with gastric dilatation-volvulus (GDV) and to determine if those findings are associated with survival. Design Retrospective study from 2000 to 2010. Setting Urban university small animal teaching hospital. Animals One hundred and one dogs diagnosed with GDV that had thoracic radiographs obtained preoperatively, and medical records available with the following information available: signalment, time of presentation, respiratory status, plasma lactate, presence of cardiac arrhythmias, reason for thoracic radiographs, radiographic findings, and outcome. Interventions None. Results Findings on preoperative thoracic radiographs included small vena cava (40%), esophageal dilation (39%), microcardia (34%), aspiration pneumonia (14%), cardiomegaly (5%), pulmonary nodule (4%), pulmonary edema (2%), sternal lymphadenopathy (1%), and pulmonary bullae (1%). Eighty-four percent of dogs (85 out of 101) survived to discharge. Dogs without cardiomegaly on presenting thoracic radiographs had a 10.2 greater odds of surviving to discharge. Conclusions The most common findings on preoperative thoracic radiographs include esophageal dilation, microcardia, and a small vena cava while the incidence of pulmonary nodules was low. A negative association between survival and presence of cardiomegaly on preoperative thoracic radiographs in dogs with GDV supports the need to obtain these images for prognostic information in spite of the emergency surgical nature of the GDV. The main limitations of this study include the possibilities of type I and type II errors, the retrospective nature of the study, and the lack of well-defined criteria for obtaining thoracic radiographs.
Uplink Throughput Enhancement by Employing Transmit-only AUX Antennas at Nomadic Terminals: A Capacity Analysis

Employing transmit only AUX antennas is a rela-tively cheap way of enhancing the communication throughput of lightweight UAVs, nodes in unmanned field expeditions, racing circuits etc, where increased data-rate demands from the nomadic terminals to the base station (BS) may happen intermittently. Asymmetric load demands in the uplink often call for augmented infrastructure at the nomadic terminals or UEs. By omitting multiple receive chains onboard (along with the requirements of oversampling, synchronization and heterodyning), considerable savings in energy, ergonomics and cost is afforded by employing commercially available transmit only AUX antennas, along with the MAIN antenna subsystem possessing its own dedicated receive chain. The absence of AUX receive chains brings new design chal-lenges, for example, estimating the full channel becomes trouble-some at the UE, particularly when the BS has a large number of antennas. On the other hand, the MIMO capacity at high SNRs, effectively the full degrees of freedom, can be achieved even when the channel state information (CSI) is available only at the receiver, for many scattering environments. This motivates us to analyze the situation where the receiving BS has full CSI, however the UE transmitter only knows the CSI from its main antenna system. Channel statistics are assumed to be known at all the terminals. We investigate the uplink ergodic Shannon capacity of the resulting partial CSI MIMO system, and propose transmission schemes which are almost optimal, at all SNRs of interest. Our results show that even using a single additional AUX can provide 60 -70% capacity improvement for a receiver with more than two receive antennas, even at moderate SNRs.
Ferroptosis: A Promising Therapeutic Target for Neonatal Hypoxic-Ischemic Brain Injury

Ferroptosis is a type of programmed cell death caused by phospholipid peroxidation that has been implicated as a mechanism in several diseases resulting from ischemic-reperfusion injury. Most recently, ferroptosis has been identified as a possible key injury mechanism in neonatal hypoxic-ischemic brain injury (HIBI). This review summarizes the current literature regarding the different ferroptotic pathways, how they may be activated after neonatal HIBI, and which current or investigative interventions may attenuate ferroptotic cell death associated with neonatal HIBI.
Privacy-Assured and Multi-Prior Recovered Compressed Sensing for Image Compression-Encryption Applications

Compressed sensing (CS), a popular signal processing technique, can achieve compression and encryption simultaneously. Therefore, it has extension applications in various fields. However, CS is vulnerable to cryptographic attacks for its linear encoding process. To solve this problem, a permutation-diffusion structure is designed and embedded to the CS encoding process. In addition, it can increase the key space while compressing. Since the permutation-diffusion structure reduces the sparseness, superior recovery performance cannot be achieved. Therefore, the multi-prior regularization recovery strategy is designed to improve the recovery performance, where the multi-prior regularization term denotes l1 norm, total variation (TV) and low rank. The simulation results and analyses demonstrate that the proposed encoding scheme can resist cryptographic attacks, increase the key space while compressing, and achieve 1.54dB PSNR gain on average in comparison with the existing schemes.
Enhanced Detection of Cardiac Surgery-Associated Acute Kidney Injury by a Composite Biomarker Panel in Patients with Normal Preoperative Kidney Function

We have recently shown that minor subclinical creatinine dynamic changes enable the excellent detection of acute kidney injury (AKI) within 6–12 h after cardiac surgery. The aim of the present study was to examine a combination of neutrophil gelatinase-associated lipocalin (NGAL), cystatin C (CysC) and creatinine for enhanced AKI detection early after cardiac surgery. Elective patients with normal renal function undergoing cardiac surgery using cardiopulmonary bypass were enrolled. Concentrations of plasma NGAL, serum CysC and serum creatinine were determined after the induction of general anesthesia, at the termination of the cardiopulmonary bypass and 2 h thereafter. Out of 119 enrolled patients, 51 (43%) developed AKI. A model utilizing an NGAL, CysC and creatinine triple biomarker panel including sequential relative changes provides a better prediction of cardiac surgery-associated acute kidney injury than any biomarker alone already 2 h after the termination of the cardiopulmonary bypass. The area under the receiver-operator curve was 0.77, sensitivity 77% and specificity 68%.
Vowel length affects pre-boundary lengthening in Czech

This study investigates the patterns of pre-boundary lengthening (PBL) in Czech, a language with phonemic length distinctions. Studies on Finno-Ugric languages found that PBL can be restricted or absent on vowels for which a corresponding longer phoneme with the same quality exists in the inventory. The present study provides the first investigation of a Slavic language in this regard. A production experiment was conducted that tested for an impact of vowel length on the presence and amount of PBL in trisyllabic words with antepenultimate stress. The results showed that short and long vowels undergo lengthening in all positions, but short vowels in pre-final syllables tend to involve a smaller amount of relative lengthening than long vowels. Furthermore, the results are compatible with the assumption that the initiation of PBL is linked to the stressed syllable, as has been observed in Germanic languages. The initiation point, however, shifts to the following syllable if the distance between the stressed syllable and the end of the word is increased.
Criticality-Based Advice in Reinforcement Learning (Student Abstract)

One of the ways to make reinforcement learning (RL) more efficient is by utilizing human advice. Because human advice is expensive, the central question in advice-based reinforcement learning is, how to decide in which states the agent should ask for advice. To approach this challenge, various advice strategies have been proposed. Although all of these strategies distribute advice more efficiently than naive strategies, they rely solely on the agent's estimate of the action-value function, and therefore, are rather inefficient when this estimate is not accurate, in particular, in the early stages of the learning process. To address this weakness, we present an approach to advice-based RL, in which the human’s role is not limited to giving advice in chosen states, but also includes hinting a-priori, before the learning procedure, in which sub-domains of the state space the agent might require more advice. For this purpose we use the concept of critical: states in which choosing the proper action is more important than in other states.
Current Scenario of Pathogen Detection Techniques in Agro-Food Sector

Over the past-decade, agricultural products (such as vegetables and fruits) have been reported as the major vehicles for foodborne diseases, which are limiting food resources. The spread of infectious diseases due to foodborne pathogens poses a global threat to human health and the economy. The accurate and timely detection of infectious disease and of causative pathogens is crucial in the prevention and treatment of disease. Negligence in the detection of pathogenic substances can be catastrophic and lead to a pandemic. Despite the revolution in health diagnostics, much attention has been paid to the agro-food sector regarding the detection of food contaminants (such as pathogens). The conventional analytical techniques for pathogen detection are reliable and still in operation. However, laborious procedures and time-consuming detection via these approaches emphasize the need for simple, easy-to-use, and affordable detection techniques. The rapid detection of pathogens from food is essential to avoid the morbidity and mortality originating from the suboptimal nature of empiric pathogen treatment. This review critically discusses both the conventional and emerging bio-molecular approaches for pathogen detection in agro-food.
Recent Breakthrough of Bismuth-Based Nanostructured Materials for Multimodal Theranostic Applications

Cancer is a lethal disease ravaging mankind claiming millions of lives. Most frequent methods of management include surgery, radiotherapy, chemotherapy, or a combination of all the above-mentioned methods. However, there is no specific medication available to cure this condition completely and several compounds and drugs are constantly explored for their therapeutic effects. Recently, photothermal therapy, photodynamic therapy, radiotherapy, targeted drug delivery, and hyperthermia have shown to be of great interest in cancer treatment. In this direction, bismuth oxide (Bi2O3) nanoparticles can be a promising option in cancer treatment and diagnosis as well. Bi is a well-known radioactive isotope; this emits high-energy gamma (γ) rays to the affected cells. This technology can pair with existing chemotherapy to enhance the therapeutic efficacy.
A deep learning based approach for Arabic Sign language alphabet recognition using electromyographic signals

Unlike spoken languages, Sign languages are mainly based on gestures. This visual aspect makes their recognition a challenge for researchers. Despite the great advances in language processing, sign language recognition still requires a lot of work to achieve acceptable accuracy. On this work we aim to contribute on the improvement of the Arabic sign language recognition. In fact, we aim to exploit the electromyographic EMG signal produced by our muscles while making a gesture to recognize the gesture. Electromyographic signals are rich in information that can be used to calculate muscle activation. They present a potential technology for gesture recognition and classification. These signals are widely used to manufacture human-computer interaction (HMI) devices to control a prosthesis, a robot, or a computer to perform certain predefined tasks. The main objective of our research is to propose a new approach based on deep learning to recognize the Arabic sign language alphabet. We have proposed a novel convolutional neural network that uses LSTM to process feature dependencies. This model is applied to recognize 7 alphabetic characters of Arabic Sign Language. Compared to existing approaches, our approach shows a high accuracy of 97.5%.
Graph-Based Point Tracker for 3D Object Tracking in Point Clouds

In this paper, a new deep learning network named as graph-based point tracker (GPT) is proposed for 3D object tracking in point clouds. GPT is not based on Siamese network applied to template and search area, but it is based on the transfer of target clue from the template to the search area. GPT is end-to-end trainable. GPT has two new modules: graph feature augmentation (GFA) and improved target clue (ITC) module. The key idea of GFA is to exploit one-to-many relationship between template and search area points using a bipartite graph. In GFA, edge features of the bipartite graph are generated by transferring the target clues of template points to search area points through edge convolution. It captures the relationship between template and search area points effectively from the perspective of geometry and shape of two point clouds. The second module is ITC. The key idea of ITC is to embed the information of the center of the target into the edges of the bipartite graph via Hough voting, strengthening the discriminative power of GFA. Both modules significantly contribute to the improvement of GPT by transferring geometric and shape information including target center from target template to search area effectively. Experiments on the KITTI tracking dataset show that GPT achieves state-of-the-art performance and can run in real-time.