Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Influence of Information Quality, Information Seeking, and Familiarity with Purchase Intentions: A Perspective of Instagram Users in Indonesia

Instagram is a combination of commercial features with customer-oriented social computing that is often used by the Indonesian people to purchase products online. Instagram can be used by sellers and marketers to market and sell their products. This research proposed strategies that can support sellers and marketers by analyzing the factors that influence Purchase Intention on Instagram, including (1) Hedonic Motivation, (2) Review, (3) Information Quality, (4) Media Richness, (5) Trust, (6) Familiarity, and (7) Information Seeking. The data obtained in this study were processed with Partial Least Square-Structural Equation Modeling (PLSSEM). The results of this study are Information Quality, Information Seeking, and Familiarity were proven to influence Purchase Intention on Instagram users in Indonesia.
Analysis of Metabolites and Gene Expression Changes Relative to Apricot (Prunus armeniaca L.) Fruit Quality During Development and Ripening

Apricot (Prunus armeniaca L.) is a valuable worldwide agronomical crop, with a delicious fruit highlighted as a functional food with both nutritional and bioactive properties, remarkably beneficial to human health. Apricot fruit ripening is a coordinated developmental process which requires change in the expression of hundreds to thousands of genes to modify many biochemical and physiological processes arising from quality characteristics in ripe fruit. In addition, enhancing fruit and nutraceutical quality is one of the central objectives to be improved in the new varieties developed by breeding programs. In this study we analyzed the contents of main metabolites linked to the nutraceutical value of apricot fruits, together with the most important pomological characteristics and biochemical contents of fruit during the ripening process in two contrasted apricot genotypes. Additionally, the gene expression changes were analyzed using RNA-Seq and real time qPCR. Results showed that genes with differential expression in the biosynthetic pathways, such as phenylpropanoids, flavonoids, starch and sucrose and carotenoid metabolism, could be possible candidates as molecular markers of fruit quality characteristics for fruit color and soluble solid content. The gene involves in carotenoid metabolism carotenoid cleavage dioxygenase 4, and the gene sucrose synthase in starch and sucrose metabolism were identified as candidate genes in the ripening process for white skin ground color and flesh color and high soluble sugar content. The application of these candidate genes on marker-assisted selection in apricot breeding programs may contribute to the early selection of high-quality fruit genotypes with suitable nutraceutical values.
Wireless information and power transfer using single and multiple path relays

A relay‐based wireless communication model can relay information along with the power bidirectionally using the amplify‐and‐forward scheme. This paper studies such model extensively. Three information and power relaying protocols, that is, time‐based switching relaying, power‐based splitting relaying, and hybrid time switching‐based and power splitting‐based relaying (HTPSR) are used to carry forward bidirectional information and power transfer. First, a solo relay model is studied, for which, we derived a throughput expression for end‐to‐end information transfer, and this is done for all the three relaying protocols. The paper indicates that the system throughput depends upon the time switching and the power splitting ratio.
Cold Chain Logistics Path Optimization via Improved Multi-Objective Ant Colony Algorithm

In order to improve the performance and change the current situation of the cost minimization model widely used in the cold chain logistics distribution process, a multi-objective optimization model based on cost, carbon emissions and customer satisfaction is proposed. Considering the characteristic of this proposed optimization model, we design an improved ant colony algorithm with a multi-objective heuristic function to solve it, termed as ACOMO. Experimental results show that the proposed ACOMO can effectively solve the vehicle routing problem of the multi-objective optimization model, and outperforms the classic ant colony algorithms, resulting in more Pareto optimal solutions. It offers an environmentally friendly distribution solution for the problem. Specifically, the distribution path obtained by the improved ant colony algorithm manages to achieve the above multiple goals, including reduction of distribution costs and carbon emissions, and improvement of customer satisfaction. In addition, compared with a single-target model that only provides one single distribution route to cost minimization, multi-objective optimization can provide a variety of distribution route options for logistics companies in practice. Finally, through the sensitivity analysis of temperature changes and cargo damage coefficients, the proposed system successfully provides reference for the optimization of the path of cold chain logistics enterprises, and promotes logistics enterprises to effectively arrange their work and to be more socially responsible.
High Aluminum Drives Different Rhizobacterial Communities Between Aluminum-Tolerant and Aluminum-Sensitive Wild Soybean

Aluminum (Al)-resistant plant cultivars can recruit beneficial microbes to alleviate the stresses. However, the mechanism of how rhizobacterial communities strengthen Al tolerance of wild soybean has not been addressed. The aim of this study was to investigate the bacterial community structure in the rhizosphere of Al-tolerant (BW69) and Al-sensitive (W270) wild soybean germplasm subjected to three Al concentrations. We analyzed the rhizobacterial communities of the two genotypes by high-throughput sequencing of 16S rRNA genes. The results showed that high Al stress recruited different rhizobacterial communities between two genotypes. In total, 49 OTUs, such as OTU15 (Gammaproteobacteria_KF-JG30-C25_norank), OTU23 (Mizugakiibacter), and OTU93 (Alkanibacter), were enriched in the rhizosphere of BW69 at the low and high Al concentrations. Moreover, bacterial community in the rhizosphere of BW69 had a more complex co-occurrence network than did W270 at the high Al concentration. Overall, our findings highlighted that high Al concentration magnified the difference in rhizobacterial community structure between two genotypes. However, the lower modularity of the co-occurrence network in rhizosphere of BW69 than W270 under Al stress may cause the rhizobacterial community to be less resistant and more influenced by disturbance. This study emphasizes the possibility of using rhizobacteria as an improved crop breeding or gene to produce crops that are more resistant to the toxicity of heavy metal.
Partitioning Models for General Medium-Grain Parallel Sparse Tensor Decomposition

The focus of this article is efficient parallelization of the canonical polyadic decomposition algorithm utilizing the alternating least squares method for sparse tensors on distributed-memory architectures. We propose a hypergraph model for general medium-grain partitioning which does not enforce any topological constraint on the partitioning. The proposed model is based on splitting the given tensor into nonzero-disjoint component tensors. Then a mode-dependent coarse-grain hypergraph is constructed for each component tensor. A net amalgamation operation is proposed to form a composite medium-grain hypergraph from these mode-dependent coarse-grain hypergraphs to correctly encapsulate the minimization of the communication volume. We propose a heuristic which splits the nonzeros of dense slices to obtain sparse slices in component tensors. So we partially attain slice coherency at (sub)slice level since partitioning is performed on (sub)slices instead of individual nonzeros. We also utilize the well-known recursive-bipartitioning framework to improve the quality of the splitting heuristic. Finally, we propose a medium-grain tripartite graph model with the aim of a faster partitioning at the expense of increasing the total communication volume. Parallel experiments conducted on 10 real-world tensors on up to 1024 processors confirm the validity of the proposed hypergraph and graph models.
Energy Scaling of an Ultrafast All-PM-Fiber Laser Oscillator

In this article we demonstrate the first, to the best of our knowledge, all-fiber self-starting laser oscillator consisting entirely of polarization-maintaining large mode area (PLMA) fibers mode-locked with nonlinear optical loop mirror (NOLM). The system works in a Raman-free dissipative soliton regime and operates at a central wavelength of 1030 nm. It delivers stable ultrashort pulses of high energy of 12 nJ at a 7.56 MHz repetition rate which can be compressed down to the Fourier transform limit of ~250 fs. Higher energies were limited by the formation of multiple pulses in the cavity.
Improved Kernel Recursive Least Squares Algorithm Based Online Prediction for Nonstationary Time Series

We present an improved kernel recursive least squares (KRLS) algorithm for the online prediction of nonstationary time series. In order to adaptively sparsify a selected kernel dictionary for the KRLS algorithm, the approximate linear dependency (ALD) criterion based KRLS algorithm is combined with the quantized kernel recursive least squares algorithm to provide an initial framework. In order to sufficiently track the strongly changeable dynamic characteristics due to nonstationarity, a forgetting factor is further inserted into the proposed combined algorithm. It is shown that our proposed algorithm, referred to as the FFIKRLS algorithm, provides a clearly compatible algorithm structure and can be improved by the existing modeling techniques from both mapping and weights updating perspectives. Numerical simulations using benchmark Lorenz time series in comparison with existing methods have demonstrated that the proposed algorithm has superior performance in terms of both predictive accuracy and kernel dictionary size.
An improved distance‐based ant colony optimization routing for vehicular ad hoc networks

Vehicular ad hoc network (VANET) has earned tremendous attraction in the recent period due to its usage in a wireless intelligent transportation system. VANET is a unique form of mobile ad hoc network (MANET). Routing issues such as high mobility of nodes, frequent path breaks, the blind broadcasting of messages, and bandwidth constraints in VANET increase communication cost, frequent path failure, and overhead and decrease efficiency in routing, and shortest path in routing provides solutions to overcome all these problems. Finding the shortest path between source and destination in the VANET road scenario is a challenging task. Long path increases network overhead, communication cost, and frequent path failure and decreases routing efficiency. To increase efficiency in routing a novel, improved distance‐based ant colony optimization routing (IDBACOR) is proposed. The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO). The modified ACO method is a metaheuristic approach, motivated by the natural behavior of ants. The simulation result indicates that the overall performance of our proposed scheme is better than ant colony optimization (ACO), opposition‐based ant colony optimization (OACO), and greedy routing with ant colony optimization (GRACO) in terms of throughput, average communication cost, average propagation delay, average routing overhead, and average packet delivery ratio.
Modeling Incoherent Discourse in Non-Affective Psychosis

Background Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis. Methods Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects. Results Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder. Conclusions Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.
Internet of Things offloading: Ongoing issues, opportunities, and future challenges

Internet of Things (IoT) has very remarkable advantages over customary communication technologies. However, IoT suffers from different issues, such as limited battery life, low storage capacity, and little computing capacity. For this reason, in many IoT applications and devices, we require an alternative unit to execute the tasks from the user's device and return results. In general, the problem of limited resources by transferring the computation workload to other devices/systems with better resources is addressed by offloading computation. It can be focused on improving the application, extending battery life, or expanding storage capacity. The offloading operation can be performed based on various quality of service (QoS) parameters that contain computational demands for load balancing, response time, application, energy consumption, latency, and other things. Moreover, the systematic literature review (SLR) method is used to identify, assess, and integrate findings from all relevant studies that address one or more research questions on IoT offloading and conduct a comprehensive study of empirical research on offloading techniques. However, we present a new taxonomy for them based on offloading decision mechanisms and overall architectures. Furthermore, we offer a parametric comparison for the offloading methods. As well, we present the future direction and research opportunities in IoT offloading computation. This survey will assist academics and practitioners to directly understand the progress in IoT offloading.
The Relationship Between Sustainable Development Goals and Migration. An EU-28 Perspective

Abstract This study approaches migration from the perspective of an effect of the attainment of Sustainable Development Goals. In order to analyse this relationship, several indicators used to measure the SDGs attainment level were employed. These indicators are also the ones related to migration. The results show that most indicators that are used to measure the achievement of SDGs have an impact on the migration flow. Consequently, GDP per capita and unemployment rate, as main indicators used to measure the Goal 8 (Sustainable economic growth, productive employment and decent work for all), have significant impact on migration, being main factors of immigration. The main conclusion is that there is a bilateral relationship between migration and SDGs. Not only that migration can influence the achievement of sustainable development, but, at the same time, it is itself influenced by how well the Sustainable Development Goals are attained.
Epidural Naloxone Attenuates Fentanyl Induced PONV in Patients Undergoing Lower Limb Orthopaedic Surgeries. a Prospective Randomized Double-Blind Comparative Study

Abstract Background and aim Epidural administration of opioids with local anaesthetics is a popular choice for perioperative pain relief. But opioid induced side effects limit their use for postoperative analgesia. Hence, this study was designed to evaluate the effectiveness of epidural naloxone, an opioid receptor antagonist, in reducing PONV in patients receiving epidural fentanyl. Methods After obtaining the Institutional Ethics Committee approval and written informed consent, 46 patients, between 18–80 years, of either sex, with ASA physical status 1–3, undergoing lower limb orthopaedic surgeries were enlisted for this prospective, randomized, double blind comparative study. Subjects were allocated to one of the two groups and received epidurally, either fentanyl with bupivacaine (Group C, n = 23) or fentanyl with bupivacaine and naloxone 2 mcg (Group N, n = 25), for reducing postoperative pain. PONV score and Wong Bakers Scale (WBS) for pain score were recorded at 6, 12 and 18hrs, postoperatively. Results All patients were comparable with respect to age, gender, ASA PS, height, body weight as well as duration of surgery. A statistically significant decrease in PONV score was observed in Group N at 6 and 12 hours, postoperatively. The patients who required rescue antiemetic were also significantly lower in Group N at 6 and 12 hours. The mean WBS score for pain also showed significant reduction in Group N at 6 hours, postoperatively. Conclusion Concomitant use of low dose epidural naloxone and fentanyl is effective in attenuating PONV, besides enhancing analgesia in the earlypostoperative period.
Telecommunication Service Subscriber Churn Likelihood Prediction Analysis Using Diverse Machine Learning Model

The biggest problem that occurs in the telecommunication industry is increased level of customer churn. This is a very important problem that must be resolved by the company because customers who stop will have an impact on company retention. The usage of the machine learning model will certainly be able to help to predict customer trends and making precise decisions in the future. To get good results, this study is analyzed with one algorithm that had never been analyzed in previous studies to make predictions, namely Deep Neural Network (DNN). DNN compared to models that have been tested before, Random Forest and Extreme Gradient Boosting (XGBoost). This research analyzed the importance of the features, the handling toward the selection of appropriate features, and simplified the process of gathering data. The proposed model was trained and tested over Google Colaboratory using TensorFlow backend. The testing that has been done produces very good results for the Deep Neural Network (DNN) model, with a process of 68 seconds and an accuracy of 80.62%. Extreme Gradient Boosting (XGBoost) produces 76.45% accuracy with a processing time of 175 seconds, and random forest produces 77.87% with a sufficiently long processing time of up to 529 seconds.
A Comprehensive Survey of “Metamaterial Transmission-Line Based Antennas: Design, Challenges, and Applications”

In this review paper, a comprehensive study on the concept, theory, and applications of composite right/left-handed transmission lines (CRLH-TLs) by considering their use in antenna system designs have been provided. It is shown that CRLH-TLs with negative permittivity <inline-formula> <tex-math notation="LaTeX">$(\varepsilon < 0)$ </tex-math></inline-formula> and negative permeability <inline-formula> <tex-math notation="LaTeX">$(\mu < 0)$ </tex-math></inline-formula> have unique properties that do not occur naturally. Therefore, they are referred to as artificial structures called “metamaterials”. These artificial structures include series left-handed (<italic>LH</italic>) capacitances <inline-formula> <tex-math notation="LaTeX">${(C}_{L})$ </tex-math></inline-formula>, shunt <italic>LH</italic> inductances <inline-formula> <tex-math notation="LaTeX">${(L}_{L})$ </tex-math></inline-formula>, series right-handed (<italic>RH</italic>) inductances <inline-formula> <tex-math notation="LaTeX">${(L}_{R})$ </tex-math></inline-formula>, and shunt <italic>RH</italic> capacitances <inline-formula> <tex-math notation="LaTeX">${(C}_{R})$ </tex-math></inline-formula> that are realized by slots or interdigital capacitors, stubs or via-holes, unwanted current flowing on the surface, and gap distance between the surface and ground-plane, respectively. In the most cases, it is also shown that structures based on <italic>CRLH metamaterial-TLs</italic> are superior than their conventional alternatives, since they have smaller dimensions, lower-profile, wider bandwidth, better radiation patterns, higher gain and efficiency, which make them easier and more cost-effective to manufacture and mass produce. Hence, a broad range of metamaterial-based design possibilities are introduced to highlight the improvement of the performance parameters that are rare and not often discussed in available literature. Therefore, this survey provides a wide overview of key early-stage concepts of metematerial-based designs as a thorough reference for specialist antennas and microwave circuits designers. To analyze the critical features of metamaterial theory and concept, several examples are used. Comparisons on the basis of physical size, bandwidth, materials, gain, efficiency, and radiation patterns are made for all the examples that are based on <italic>CRLH metamaterial-TLs</italic>. As revealed in all the metematerial design examples, foot-print area decrement is an important issue of study that have a strong impact for the enlargement of the next generation wireless communication systems.
Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.