Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models

Parameter estimation of photovoltaic (PV) models from experimental current versus voltage (I-V) characteristic curves acts a pivotal part in the modeling a PV system and optimizing its performance. Although many methods have been proposed for solving this PV model parameter estimation problem, it is still challenging to determine highly accurate and reliable solutions. In this paper, this problem is firstly transformed into an optimization problem, and an objective function (OF) is formulated to quantify the overall difference between the experimental and simulated current data. And then, to enhance the performance of original cuckoo search algorithm (CSA), a novel improved cuckoo search algorithm (ImCSA) is proposed, by combining three strategies with CSA. In ImCSA, a quasi-opposition based learning (QOBL) scheme is employed in the population initialization step of CSA. Moreover, a dynamic adaptation strategy is developed and introduced for the step size without Levy flight step in original CSA. A dynamic adjustment mechanism for the fraction probability (Pa) is proposed to achieve better tradeoff between the exploration and exploitation to increase searching ability. Afterwards, the proposed ImCSA is used for solving the problem of estimating parameters of PV models based on experimental I-V data. Finally, the proposed ImCSA has been demonstrated on the parameter identification of various PV models, i.e., single diode model (SDM), double diode model (DDM) and PV module model (PMM). Experimental results indicate that the proposed ImCSA outperforms the original CSA and its superior performance in comparison with other state-of-the-art algorithms, and they also show that our proposed ImCSA is capable of finding the best values of parameters for the PV models in such effective way for giving the best possible approximation to the experimental I-V data of real PV cells and modules. Therefore, the proposed ImCSA can be considered as a promising alternative to accurately and reliably estimate parameters of PV models.
Effect of Sea‐Level Change on River Avulsions and Stratigraphy for an Experimental Lowland Delta

Lowland deltas experience natural diversions in river course known as avulsions. River avulsions pose catastrophic flood hazards and redistribute sediment that is vital for sustaining land in the face of sea‐level rise. Avulsions also affect deltaic stratigraphic architecture and the preservation of sea‐level cycles in the sedimentary record. Here, we present results from an experimental lowland delta with persistent backwater effects and systematic changes in the rates of sea‐level rise and fall. River avulsions repeatedly occurred where and when the river aggraded to a height of nearly half the channel depth, giving rise to a preferential avulsion node within the backwater zone regardless of sea‐level change. As sea‐level rise accelerated, the river responded by avulsing more frequently until reaching a maximum frequency limited by the upstream sediment supply. Experimental results support recent models, field observations, and experiments, and suggest anthropogenic sea‐level rise will introduce more frequent avulsion hazards farther inland than observed in recent history. The experiment also demonstrated that avulsions can occur during sea‐level fall—even within the confines of an incised valley—provided the offshore basin is shallow enough to allow the shoreline to prograde and the river to aggrade. Avulsions create erosional surfaces within stratigraphy that bound beds reflecting the amount of deposition between avulsions. Avulsion‐induced scours overprint erosional surfaces from sea‐level fall, except when the cumulative drop in sea‐level is greater than the channel depth and less than the basin depth. Results imply sea‐level signals outside this range are removed or distorted in delta deposits.
The complex lipid, SPPCT-800, reduces lung damage, improves pulmonary function and decreases pro-inflammatory cytokines in the murine LPS-induced acute respiratory distress syndrome (ARDS) model

Abstract Context Acute respiratory distress syndrome (ARDS) is a highly fatal, inflammatory condition of lungs with multiple causes. There is no adequate treatment. Objective Using the murine LPS-induced ARDS model, we investigate SPPCT-800 (a complex lipid) as treatment for ARDS. Materials and methods C57B16/N mice received 50 μg of Escherichia coli O111:B4 lipopolysaccharide (LPS). SPPCT-800 was given as either: (1) 20 or 200 mg/kg dose 3 h after LPS; (2) 200 mg/kg (prophylactically) 30 min before LPS; or (3) eight 200 mg/kg treatments over 72 h. Controls received saline installations. Results At 48 and 72 h, SpO2 was 94% and 90% in controls compared to 97% and 94% in treated animals. Expiration times, at 24 and 48 h, were 160 and 137 msec for controls, but 139 and 107 msec with SPPCT-800. In BALF (24 h), cell counts were 4.7 × 106 (controls) and 2.9 × 106 (treated); protein levels were 1.5 mg (controls) and 0.4 mg (treated); and IL-6 was 942 ± 194 pg/mL (controls) versus 850 ± 212 pg/mL (treated) [at 72 h, 4664 ± 2591 pg/mL (controls) versus 276 ± 151 pg/mL (treated)]. Weight losses, at 48 and 72 h, were 20% and 18% (controls), but 14% and 8% (treated). Lung injury scores, at 24 and 72 h, were 1.4 and 3.0 (controls) and 0.3 and 2.2 (treated). Discussion and conclusions SPPCT-800 was effective in reducing manifestations of ARDS. SPPCT-800 should be further investigated as therapy for ARDS, especially in longer duration or higher cumulative dose studies.
Spectral analysis for diagnosis of bearing defects in induction machine drives

In this study, the finite element method (FEM) is utilised for precise analysis and detection of the bearing faults in induction machine drives. For this purpose, the machine inductances in the presence of slots, teeth, and various bearing failures are obtained by FEM. In addition, the spectrum of the stator currents are analysed by employing the electrical model of the induction machine drives. Some experimental and numerical results are illustrated that the proposed model can consider small bearing failures in high-noise environments and thus can be used for analysing and detecting bearing failures in induction machine drives.
The Investigator's Dilemma - A Review of Social Media Analytics Research Ethics in Information Systems

Social media have become not only integral parts of our private and professional lives, but also an indispensable source of data for empirical research across a variety of academic disciplines. Applying a Social Media Analytics (SMA) methodology, however, imposes heavy ethical challenges on researchers. Scholars in the Information Systems (IS) discipline must deal with a patchwork of ethical frameworks, regulations, and (missing) institutional support. To initiate a debate on how to develop a common understanding of SMA research ethics, this paper compiles a scoping review of extant literature and suggests a research agenda for IS scholarship on this matter. The review yields a total of eight fundamental principles of ethical SMA research, which provide a starting point to guiding individual researchers towards more ethical conduct. At the same time, this work unearths a multitude of intricate dilemmas that are currently unresolved. The findings of this review will encourage IS scholarship to find its own voice in the debate about social media research ethics.
Nonlinear Consensus-Based Connected Vehicle Platoon Control Incorporating Car-Following Interactions and Heterogeneous Time Delays

This paper proposes a distributed nonlinear consensus delay-dependent control algorithm for a connected vehicle (CV) platoon. In particular, considering that the behavior of the following vehicle is associated with the longitudinal inter-vehicle gap with respect to the preceding vehicle, a nonlinear function is designed to characterize the car-following interactions between CVs. Then, a nonlinear consensus algorithm is proposed by incorporating the car-following interactions and heterogeneous time delays. The delay-dependent convergence condition of the proposed control algorithm is analyzed using the Lyapunov–Krasovskii method, and an estimate of the delay bound is provided. Under the proposed algorithm, not only can the consensus of CVs be guaranteed but also the behavior of vehicles is consistent with traffic flow theory. Finally, an example using a 10-vehicle platoon is provided under three scenarios: no time delays, heterogeneous time delays, and homogeneous time delays. Results from extensive simulations verify the effectiveness of the proposed control algorithm in terms of the position, velocity, and acceleration/deceleration profiles.
Online Gradient Descent for Online Portfolio Optimization with Transaction Costs

Outperforming the markets through active investment strategies is one of the main challenges in finance. The random movements of assets and the unpredictability of catalysts make it hard to perform better than the average market, therefore, in such a competitive environment, the methods designed to keep low transaction costs have a significant impact on the obtained wealth. This thesis focuses on investing techniques to beat market returns through Online Portfolio Optimization while controlling transaction costs. Such a framework differs from classical approaches as it assumes that the market has an adversarial behavior and no statistical characterization is enforced, requiring frequent rebalancing of the portfolio. Within this context, most of the existing algorithms neglect transaction costs; we show that the one which provides bounded costs make unrealistic assumptions. To deal with transaction costs, in the Online Portfolio Optimization setting, we propose the use of the Online Gradient Descent algorithm. We show that it has regret, considering costs, of the order O( √ T ), T being the investment horizon, and has Θ(N) per-step computational complexity, N being the number of assets. Furthermore, we show that this algorithm provides competitive gains when compared empirically with state-of-the-art online learning algorithms on real-world datasets.
A spectral-based clustering algorithm for directed graphs

Fundamentally, a graph is a set of vertices and edges, where each edge connects two vertices. Graphs can be undirected (where if vertex v is connected to vertex u then vertex u is connected to vertex v), or directed (where an edge from v to u does not necessarily imply the converse). Graphs can be used to represent data in a variety of applications; examples include users in a social network (where nodes are users and edges are friends) to functional connectivity in the brain (where nodes are brain areas and edges represent connection strengths between areas). 1 A fundamental algorithmic graph theory problem is clustering: how can we define the notion of a “cluster” in a graph, and how can we design efficient algorithms to identify such clusters? Clustering is a natural choice to analyze graph-based datasets. Returning to our previous two examples, clusters can represent friend groups in a social network or brain areas with spatiotemporally correlated activity.
Close relationship between the dressed metric and the hybrid approach to perturbations in effective loop quantum cosmology

The dressed metric and the hybrid approach to perturbations are the two main approaches to capture the eﬀects of quantum geometry in the primordial power spectrum in loop quantum cosmology. Both consider Fock quantized perturbations over a loop quantized background and result in very similar predictions except for the modes which exit the horizon in the eﬀective spacetime in the Planck regime. Understanding precise relationship between both approaches has so far remained obscured due to diﬀerences in construction and tech-nical assumptions. We explore this issue at the classical and eﬀective spacetime level for linear perturbations, ignoring backreaction, which is the level at which practical computations of the power spectrum in both of the approaches have so far been performed. We ﬁrst show that at the classical level both the approaches lead to the same Hamiltonian up to the second order in perturbations and result in the same classical mass functions in the Mukhanov-Sasaki equation on the physical solutions. At the eﬀective spacetime level, the diﬀerence in phenomenological predictions between the two approaches in the Planck regime can be traced to whether one uses the Mukhanov-Sasaki variable Q ~k (the dressed metric approach) or its rescaled version ν ~k = aQ ~k (the hybrid approach) to write the Hamiltonian of the perturbations, and associated polymerization ambiguities. It turns out that if in the dressed metric approach one chooses to work with ν ~k , the eﬀective mass function can be written exactly as in the hybrid approach, thus leading to identical phenomenological predictions in all regimes. Our results explicitly show that the dressed metric and the hybrid approaches for linear perturbations, at a practical computational level, can be seen as two sides of the same coin.
An Intelligent Sensor Mobile Phone Assisting System using AI and Machine Learning

Technology is taking over the world [7]. Thus, how elderly people can request for help when they use a mobile device if there is anybody around them? In this paper, we address this issue by providing a system that can share and remote control a mobile device in real time [8]. An Android mobile app has been developed as an assistant tool. Thus, when a user needs help, she/he uses an unique ID, sends a request and shares the mobile screen, so the helper sees the sharing screen in his/her device and assists the person who needs help. We applied our application to data analysis and accurate measurements. For the accurate measurement, we conducted diverse experiments to observe the stability of use in different devices, and the influence of geographic, environmental, and network factors. The result shows there are no interrupts during the 30 experiments, which means that the system is stable for use and the network speed is the main factor which affects the average connection delay. For the data analysis, we advertised the Mobile App in communities and schools and received a total of 20 feedback questionnaires. We observe that users from 66 - 70 yield the highest positive score.
Response of Potato Growth in Vitro to Led Spectral Exchange

This study was conducted to explore the growth behavior of plants tissue culture in vitro using an optical spectrum system exchange. In this investigation, the 3 rd generation in vitro plantlets of potato cv. "Diamond" were employed. The experiment included 5 treatments. The adventitious shoots of S. tuberosum (0.5–1 cm in length) were exposed in jars containing approximately 30 ml of the Murashige and Skoog (MS ) salt medium. The cultures were kept under following LED lights: White, Yellow, Green, Red and Blue respectively for 4 days switch within every light, compared to continuous white light for the same period (20 days). Comparing to the white light every 20 days found that the third subculture was significantly higher than the second and the first, in most optimal spectrum LED treatment color, also most averages of growth treatment was higher under red and blue LED light. the contents of amino acids and proteins exhibited accumulation trend with variant degree depend on type of light used. Generally, the combined light had the highest accumulation of amino acids and proteins compared to monochromatic light. For monochromatic light, the blue light recorded the most accumulation of amino acids and proteins compared to other lights. Also, plantlets exposed to red and blue light had the maximum levels of pigments. High accumulation of α─tocopherol content in the order, blue>red>green>yellow>white. Blue light and plants subjected to multiple light cycles were found to have the most accumulation in anthocyanin.
Multiplex PCR assay for species identification of meat and dairy products

Cases of fraudulent meat and dairy products have increased worldwide, especially in developing countries. To determine the misrepresented animal species, appropriate tools in routine monitoring should be available for food inspections. In the present work, a multiplex polymerase chain reaction assay for species identification of products from ruminants including buffalo, cattle, goat, and sheep was developed. The primer set KUMUT_cFarmSp1 was composed of five species-specific primers and a pair of positive-control primers. The primer set amplified 106-, 163-, 232-, and 308-bp specific fragments from the cytochrome b ( cyt b ) gene of buffalo, cattle, goat, and sheep, respectively, and 370-bp positive-control fragment from 16S ribosomal RNA (16S rRNA). The detection limit of this PCR assay is 0.1 ng of DNA template. The developed primer set exhibited strong specificity, sensitivity, robustness, and simplicity for food verification, thus indicating its usefulness for species verification in food quality control and law enforcement.
Knottins As A Structural Basis For The Stabilization Of Radio Pharmaceuticals

The aim of the current reseach was to study synthesize a peptide tropic to the prostate-specific antigen and containing a cysteine knot. Materials and Methods : A comparative study of the efficiency of binding in vitro with prostate cancer cells of artificial peptides based on U5-scytotoxin-Sth1a toxin with an inserted peptide tropic to the prostate-specific membrane antigen and the radiopharmaceutical 177Lu-PSMA-617 is carried out. Three prostate cancer cell lines were used in the research. Results : Synthesized DOTA-Knot/C0-C1, DOTA-Knot/C1-C2 and DOTA-Knot/C2-C3 peptides containing the GTIQPYPFSWGY sequence inserted into U5-Sth1a knottin are more stable in blood plasma and saline and also show a similar degree of binding to LNCaP, PC3 cells compared with the 177Lu-PSMA-617 radiopharmaceutical. Conclusion : Modified peptides with a peptide tropic to the PSMA antigen inserted into the structure of U5-Sth1a demonstrate the greatest stability.
Familial Deportation Experience, Undocumented Parents, and Depressive Symptoms among Early Adolescent Latinos

Objectives: Experiencing the deportation of close family members has been shown to adversely impact children’s well-being, yet the role of parental documentation status for exacerbating adverse outcomes among youth following the experience of deportation has not been investigated. Methods : Using a general population sample of Latino 7 th grade students in an urban public-school district in the South-Central U.S. (N=661), we examined the relationship between familial deportation experience and psychological distress as well as the moderating associations of parental citizenship status. Results : Ordinary least squares regression findings indicate that experiencing or witnessing the deportation of a family member is significantly associated with higher rates of depressive symptoms. Moreover, parental citizenship status has a moderating effect; depressive symptoms are magnified among youth who report that both of their parents have undocumented legal status. Conclusions: The study findings suggest that there are significant consequences for youth well-being when a parent or close family member is deported and the child remains in the U.S. Further, the compounding effects for youth whose parents are both undocumented suggests fear of deportation. Immigration policies, programs, and schools need to consider the emotional needs of youth who have undocumented parents.
ROS2SWARM - A ROS 2 Package for Swarm Robot Behaviors

Developing reusable software for mobile robots is still challenging. Even more so for swarm robots, despite the desired simplicity of the robot controllers. Prototyping and experimenting are difficult due to the multi-robot setting and often require robot-robot communication. Also, the diversity of swarm robot hardware platforms increases the need for hardware-independent software concepts. The main advantages of the commonly used robot software architecture ROS 2 are modularity and platform independence. We propose a new ROS 2 package, ROS2sWARM, for applications of swarm robotics that provides a library of ready-to-use swarm behavioral primitives. We show the successful application of our approach on three different platforms, the TurtleBot3 Burger, the TurtleBot3 Waffle Pi, and the Jackal UGV, and with a set of different behavioral primitives, such as aggregation, dispersion, and collective decision-making. The proposed approach is easy to maintain, extendable, and has good potential for simplifying swarm robotics experiments in future applications.
An efficient Serverless Architecture to support fine-grained coordination and state sharing for Stateful Applications

In today’s cloud driven work culture, serverless infrastructure is widely adopted due to its pay-as-you-go beneﬁts. In serverless resource allocation is fully managed by the cloud provider and the developer can solely concentrate on application development. To take to beneﬁt of serverless infrastructure many organizations are migrating their work on serverless. A range of applications and domains including image, text, and speech processing have led to machine learning (ML) becoming a widely deployed technology across many IT industries. It is diﬃcult to implement ML algorithms workﬂows on a serverless platform. There are clearly deﬁned user interactions during various steps in ML workﬂows, such as data preprocessing, data training, and data ﬁne-tuning. The user may execute this frequently and will require low latency and the provision of resources automatically. A serverless platform must be designed eﬃciently to support a stateful application workﬂow. There is a need for distributed shared memory layer to manage synchronization and ﬁne-grained coordination between intermediate shared data. The proposed architecture provides an eﬃcient workﬂow for stateful application with low network latency and the ability to share the mutable object with ﬁne grained coordination and synchronization. I have deployed the architecture on Amazon Web Services lambda functions. I have also validated the architecture using micro-benchmarks like latency, throughput, and parallelism. Further to state the ﬁne-grained state management in ML application on serverless I have compared its performance with AWS Spark Clusters for k-means clustering algorithm using 30 GB of data generated using spark-pref. According to the results, it was found that the proposed architecture optimizes ML application’s performance on serverless in terms of time.