Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced analytics on IV curves and electroluminescence images of photovoltaic modules using machine learning algorithms

Advanced analysis and monitoring of photovoltaic solar modules is required to maintain the reliable operations of photovoltaic plants. Hence, it requires diagnostics through current–voltage (IV) curves, electroluminescence (EL) imaging, and other measurement techniques. The analysis through IV characterization provides the discerning insight about the quantitative measure of solar module performance, while the image characterization methods on EL images can capture spatial defects with microscopic resolution such as microcracks, broken cells interconnections, shunts, among many other defect types. The fusion of these two methods with supervised and unsupervised machine learning can generate unique insight with classification, regression, and dimension reductions on IV–EL data. In this study, we have performed the IV–EL correlation by classifying the IV data based on EL image annotation (where the class information is coming from EL image). The feature vectors consist of IV curve parameters and statistical features. We have first applied the unsupervised learning algorithms t‐distributed stochastic neighbor embedding (t‐SNE) and uniform manifold approximation and projection (UMAP) for dimensionality reduction to understand the importance of various features on EL defect types. Furthermore, we had applied feature selection algorithms before applying the classification algorithms. We have performed the classification of various defect types by applying the random forests (RF) and XGBoost algorithm while identifying the top features. The accuracy was achieved greater than 91% and 95%, respectively, for supervised methods on the top five features. This correlation of IV–EL measurement could benefit in quick identification of various defect types in PV modules with only IV curve parameters, given the classification models are modeled using large‐scale datasets and tuned optimally.
Opposing effects of plant growth regulators via clonal integration on apical and basal performance in alligator weed

Invasive plants are a major threat to biodiversity and may adversely affect food security. Clonal integration enables the sharing of resources between connected ramets and can enhance plant performance in many invasive species. However, few studies have examined the role of clonal integration when weeds are exposed to plant growth regulators (PGRs). PGRs are used extensively in agriculture and may affect nearby weeds through soil leaching, erosion, and runoff. Our aim was to investigate the effects of clonal integration on growth in a noxious weed, Alternanthera philoxeroides (alligator weed), in response to two PGRs frequently used in agriculture, gibberellins (GA) and paclobutrazol (PAC). Ramets of A. philoxeroides were propagated in the greenhouse, and treated with PGRs. PGRs were applied to the older ramets (i.e., ‘basal’ part), with half of the plants having the stems between the apical (younger) and basal parts left connected, while the remaining plants had the stems between the two parts severed. Following the growing period, plants were measured for growth traits. We found that GA and PAC had contrasting effects on plant growth. GA significantly promoted above-ground growth of the apical ramets via clonal integration. Alternatively, PAC inhibited above-ground growth in the basal and apical parts, and enhanced below-ground growth of the basal and apical ramets through clonal integration. Our results highlight how clonal integration can promote growth in A. philoxeroides following the application of PGRs, which is likely an important mechanism for this species to invade new environments.
Maternal and household Predictors of Malnutrition among Under-five Children in Internally Displaced Person Camps of Adamawa and Yobe States, Nigeria

Malnutrition is a significant cause of morbidity and mortality among under-five children, especially in low- and middle-income countries (LMICs). This study aimed to assess the predictors of malnutrition among children less than five years in IDP camps of Adamawa and Yobe states, Nigeria. It was a cross-sectional study conducted from September 2018 to January 2019. A sample of 807 children aged 6 to 59 months was selected using the probability proportionate to size sampling (PPSS) technique. A structured questionnaire was administered to collect information on the children and their caregivers. The overall prevalence of stunting, wasting, and underweight were 17.1%, 40.9%, and 29.7%, respectively. The predictors of stunting were the mother's level of education and family planning usage. For wasting, identified predictors include the mother's level of education, family planning usage, family planning interest, mother's visit to the antenatal clinic, and the number of mother's visits to the antenatal clinic. In contrast, the predictors of underweight consisted of mother's level of education, family planning usage, family planning interest, mother's visit to antenatal clinic, and household income. In conclusion, it is evident from these findings that malnutrition, especially the acute form (wasting), is not only disproportionally high among under-five children but is a public health emergency that calls for urgent attention from concerned authorities. Collaborative efforts among sectors like health, agriculture, education, environment and finance; are very critical.
Rate design with distributed energy resources and electric vehicles: A Californian case study

Abstract The high penetration of distributed energy resources and electric vehicles is changing the way the electricity system is managed. In turn, the way utilities have been recovering their expenditures through tariffs needs reformulation. We investigate the impact of different retail tariff designs from a Californian scenario on private investment incentives and cost-shifting using solar PVs, stationary batteries, and electric vehicles. The private commercial facilities studied do not own the vehicles, and the vehicle owners are remunerated for energy services provided; this remuneration strongly depends on the connection hours of the vehicles and the type of tariff applied. EV net income varies annually from $57 to $218 per vehicle, reaching the highest values when stationary batteries are present and significant demand charges are applied. We found that an energy-based tariff incentivizes the adoption of solar PVs, bringing high private gains, but often with high cost-shifting. A shift towards coincident peak tariffs in the short term and capacity-based tariffs in the long run, if the cost of DERs continues to fall quickly, can alleviate cost-shifting caused by strong DER penetration. Finally, we derive policy implications from the results and earmark more sophisticated tariff designs for further investigation.
Enhancing problem‐solving skills of novice programmers in an introductory programming course

Novices should focus both on problem‐solving skills and programming knowledge (syntax and semantics) in learning to program. Most introductory programming courses pay more attention to programming knowledge. This study introduces a PAAM model (Problem Analysis Algorithmic Model) in the teaching–learning process of the introductory programming (IP) course. The model focuses on problem statement requirements (input, output, and process) and problem‐solving skills for novices. A web application was developed to offer the model in the course. Two surveys were conducted to collect the responses of IP students. Semistructured interviews were performed with the IP instructors. The final exam grades of the IP course over the last two semesters were compared to determine the impact of the PAAM model on the students gain. Results show that most students appreciated the model in the teaching–learning process of the course. The model helps students to develop problem‐solving skills (p = .002), understand problem statement inputs (p = .003), process (p = .004), outputs (p = .034), and motivates them to learn programming (p = .031). The application impacts positively on students' behavior and attitude toward completing the programming exercises in the course. Overall, the PAAM approach (11.1%) impacts positively by decreasing the attrition rate (9.9%) in the course when it is compared with the traditional approach (21.0%).
EETSP: Energy‐efficient two‐stage routing protocol for wireless sensor network‐assisted Internet of Things

Wireless sensor network (WSN) is a key technology that can support Industrial Revolution 4.0. The sensor nodes (SNs) are the important component of WSN. Recharge or repair of batteries of SNs is impossible, and thus, energy is the most valuable resource of WSN. Over the years, many methods have been proposed to conserve the precious energy resource of WSN. The most important concern in WSN is how much of the energy of the SNs can be saved, which can contribute to the better network life of WSN. WSN‐assisted IoT output is reduced when a wide region of the network is deployed. Energy‐efficient routing is one of the ways to reduce power consumption in WSNs. Therefore, a novel energy‐efficient two‐stage routing protocol (EETSP) has been proposed to reduce the energy used in WSN and extend the stability period as well as the network lifetime of WSN. Through this protocol, the number of packets communicated to the base station (BS) is significantly improved, and the cluster heads (CHs) and secondary cluster heads (SCHs) reduce the energy utilization in the network. SCH works as a backup CH. EETSP consists of two stages. The selection of CH and SCH is made in the primary stage, based on the input parameters. Intercluster routing and intracluster routing are performed in the second stage. The improvements of EETSP are examined against LEACH and Z‐SEP, and obtained results indicate that the EETSP is better in terms of stability period, throughput and network lifetime.
Photoelastic Properties of Trigonal Crystals

All possible experimental geometries of the piezo-optic effect in crystals of trigonal symmetry are studied in detail through the interferometric technique, and the corresponding expressions for the calculation of piezo-optic coefficients (POCs) πim and some sums of πim based on experimental data obtained from the samples of direct and X/45°-cuts are given. The reliability of the values of POCs is proven by the convergence of πim obtained from different experimental geometries as well as by the convergence of some sums of POCs. Because both the signs and the absolute values of POCs π14 and π41 are defined by the choice of the right crystal-physics coordinate system, we here use the system whereby the condition S14 > 0 is fulfilled (S14 is an elastic compliance coefficient). The absolute value and the sign of S14 are determined by piezo-optic interferometric method from two experimental geometries. The errors of POCs are calculated as mean square values of the errors of the half-wave stresses and the elastic term. All components of the matrix of elasto-optic coefficients pin are calculated based on POCs and elastic stiffness coefficients. The technique is tested on LiTaO3 crystal. The obtained results are compared with the corresponding data for trigonal LiNbO3 and Ca3TaGa3Si2O14 crystals.
Clinical Efficacy of Intra‐Articular Injection with P‐PRP Versus that of L‐PRP in Treating Knee Cartilage Lesion: A Randomized Controlled Trial

Platelet‐rich plasma（PRP), with different concentration of leukocytes, may lead to varying effects in the treatment of cartilage lesions. So far, current research has not shown enough evidence on this. To evaluate the clinical efficacy and safety of intra‐articular injection with pure platelet‐rich plasma (P‐PRP) versus those of leukocyte platelet‐rich plasma (L‐PRP) in treating knee cartilage lesions, we conducted a double‐blind, randomized controlled clinical trial with a larger sample and longer follow‐up period.
Study of the Density Distributions and Elastic form Factors of the Exotic Nuclei, 8He And 26F, Via the Three-Body Model

The matter, proton, and neutron density distributions of the ground state, the nuclear root-mean-square (rms) radii, and the elastic form factors of a two- neutron, 8He and 26F, halo nuclei have been studied by the three body model of within the harmonic oscillator (HO) and Woods-Saxon (WS) radial wave functions. The calculated results show that the two body model within the HO and WS radial wave functions succeeds in reproducing the neutron halo in these exotic nuclei. Moreover, the Glauber model at high energy (above several hundred MeV) has been used to calculate the rms radii and reaction cross sections of these nuclei.
Characterization of Sonic Events Present in Natural-Urban Hybrid Habitats Using UMAP and SEDnet: The Case of the Urban Wetlands

We investigated whether the use of technological tools can effectively help in manipulating the increasing volume of audio data available through the use of long field recordings. We also explored whether we can address, by using these recordings and tools, audio data analysis, feature extraction and determine predominant patterns in the data. Similarly, we explored whether we can visualize feature clusters in the data and automatically detect sonic events. Our focus was primarily on enhancing the importance of natural-urban hybrid habitats within cities, which benefit communities in various ways, specifically through the natural soundscapes of these habitats that evoke memories and reinforce a sense of belonging for inhabitants. The loss of sonic heritage can be a precursor to the extinction of biodiversity within these habitats. By quantifying changes in the soundscape of these habitats over long periods of time, we can collect relevant information linked to this eventual loss. In this respect, we developed two approaches. The first was the comparison among habitats that progressively changed from natural to urban. The second was the optimization of the field recordings’ labeling process. This was performed with labels corresponding to the annotations of classes of sonic events and their respective start and end times, including events temporarily superimposed on one another. We compared three habitats over time by using their sonic characteristics collected in field conditions. Comparisons of sonic similarity or dissimilarity among patches were made based on the Jaccard coefficient and uniform manifold approximation and projection (UMAP). Our SEDnet model achieves a F1-score of 0.79 with error rate 0.377 and with the area under PSD-ROC curve of 71.0. In terms of computational efficiency, the model is able to detect sound events from an audio file in a time of 14.49 s. With these results, we confirm the usefulness of the methods used in this work for the process of labeling field recordings.
Formation of a Creative Environment for Decision-Making in Business Based on the Construction of Intelligence Maps Using Information Technology

The study is devoted to the study of the possibilities of using smart cards in order to form a creative environment for decision-making in business. It has been proven that creative thinking is one of the most important skills that help to make innovative, non-standard decisions. This leads to the need not only to establish the requirement for creative thinking when hiring an employee, but also the formation of an environment that will realize the creative potential and constantly develop it. One of the techniques of forming a creative environment is the use of intelligence cards. The stages of construction of intelligence maps are established. Formalization of information in the form of intelligence maps with the help of special software will replenish the base of information and analytical support of the decision-making process. The analysis of the market of software applications that allow automating the process of creating smart cards. The directions of use of intelligence cards are systematized, he order is established application in accordance with the intended purpose, methods, which should be combined and Soft skills, are supported. It is concluded that intelligence cards, in addition to supporting creative thinking, allow you to practice the skills of creative thinking, teamwork, critical thinking, communication.
COVID-19 infection mortality risk in Iranian patients with type 2 diabetes, hypertension and obesity.

Background Diabetes mellitus and hypertension are highly prevalent among patients with severe COVID-19. Aims To study the mortality risk of COVID-19 infection in patients with type 2 diabetes and additive effect of hypertension and obesity in the Iranian population. Methods This was a cross-sectional survey of the national COVID-19 registry from the Iranian Ministry of Health. The medical status of 22 002 patients with COVID-19 between 1 March and 30 April 2020 was analysed. Results Patients with type 2 diabetes had a higher risk of mortality with an odds ratio (OR) of 1.67 [95% confidence interval (CI): 1.53-1.82, P < 0.001]. The risk of mortality was also high in patients with diabetes and hypertension, with an odds ratio of 1.76 (95% CI: 1.56-1.99, P < 0.001). The odds ratio of the risk of mortality in patients with diabetes, hypertension and obesity was 1.87 (95% CI: 1.35-2.58, P < 0.001). Conclusion Type 2 diabetes, hypertension and obesity each predict mortality in Iranian patients with COVID-19, and when they are present together, patients have a greater risk of mortality.
Retracted: Multiscale fast correlation filtering tracking algorithm based on a feature fusion model

Retraction: Multiscale fast correlation filtering tracking algorithm based on a feature fusion model Yuantao Chen, Jin Wang, Songjie Liu, Xi Chen, Jie Xiong, Jingbo Xie, Kai Yang, 2021, 33 (15), (https://doi.org/10.1002/cpe.5533) The above article, published online on 23 October 2019 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editors, David W. Walker, Jinjun Chen, Nitin Auluck and Martin Berzins, and John Wiley and Sons Ltd. The retraction has been agreed due to scientific errors arising from the incorrect use of materials and data, which have led to conclusions that are unreliable.
Papanin Ridge and Ojin Rise Seamounts (Northwest Pacific): Dual Hotspot Tracks Formed by the Shatsky Plume

Although previous findings support an origin of the Shatsky Rise igneous plateau (Northwest Pacific) through interaction of a mantle plume with a mid‐ocean ridge triple junction, the evidence for the involvement of a mantle plume is equivocal. The identification of an intraplate hotspot track emanating from the plateau could solve this controversy. Here we present major and trace element geochemical data from two different bathymetric features that emanate from the youngest end of Shatsky Rise: Papanin Ridge and the Ojin Rise Seamount province. Combining our results with plate tectonic reconstructions, we conclude that Papanin Ridge represents a hotspot track formed by plume‐ridge interaction. Whereas the southwestern part was formed along the path of the retreating Pacific‐Farallon‐Izanagi triple junction, the northeastern part was built by preferential drainage into its Pacific‐Farallon branch. In contrast, the Ojin Rise Seamounts formed as a true intraplate hotspot track of the Shatsky plume tail. Our wide‐ranging study reveals systematic spatial geochemical variations, consistent with a lithospheric thickness control on magma composition derived from melting a heterogeneous plume source. The recognition of two hotspot tracks and in particular of the Ojin Rise Seamounts as an intraplate hotspot track that is directly linked to Shatsky plateau volcanism both in terms of geochemistry and plate tectonic reconstructions confirms the long‐disputed involvement of a mantle plume for the formation of Shatsky Rise.
A Stochastic-Dynamic-Optimization Approach to Estimating the Capacity Value of Energy Storage

Energy storage can contribute to the resource-adequacy needs of power systems. However, the energy-limited nature of energy storage complicates estimating its resource-adequacy contribution. Energy storage that discharges to mitigate a loss-of-load event may have less energy available to mitigate a subsequent loss-of-load event. We present a stochastic-dynamic-optimization approach to capture such impacts endogenously. We demonstrate our approach using an example and two case studies, which show that energy storage's capacity value is sensitive to the load patterns of the system in which it is deployed.
Research of 6‐DOF pose estimation in stacked scenes

Recently, robot vision recognition has been widely used in modern logistics system. In the face of complex and changeable environment and objects, industrial robots need to identify and operate the target objects accurately. Therefore, this article proposes a method based on Mask R‐CNN and RGB‐D to estimate the 6‐DOF of the target object. We find the 6‐DOF of the object is identified and located accurately.
Production of essential oils from Ceylon lemongrass (Cymbopogon nardus) in Loumo district (Congo)

The objective was to assess the social, economic and environmental impact of the production of essential oil (ET) of Ceylon citronella in Loumo . A field survey was carried out with 60 EO producers in 9 localities in the Loumo district in Congo. The results show that 73 % of producers are adult men , 37% of whom are between 51 and 60 years old. The schooling rate is 98%, with a predominance of producers with secondary education levels, the majority living in a common-law relationship. The production of EO is a secondary activity to agriculture and commerce. The income generated by this activity is marginal but makes it possible to diversify the income of the actors. This activity is still in the embryonic stage. The low economic performance translates into low annual investments and low annual distillation frequencies, a consequence of the presence of a single EO extractor. The production of 1.75 litre of essential oil requires 140 kg of cooking wood, which has a direct impact on the surrounding forest ecosystems. The promotion of fast-growing species is necessary in view of the development of this sector.
Cost Data Visualisation

Decision making using the methodologies and analysis generated by the cost engineering function is widely considered good practice across industry, as a way to support both technical engineering decisions and fundamental business decisions. One persistent challenge for the professional cost engineer is to present cost data and information to decision makers and a mix of audiences. Data visualisation is therefore an important element to ensure that data is presented in a clear, effective and convenient format to ensure sufficient insights can be gathered. This work explores different data presentation and visualisation approaches. This review highlighted this topic as a research gap that this paper is novel in addressing. The review findings are further explored through a series of semi-structured interviews with experts in relevant fields to establish effective data visualisation methods, along with the challenges associated with presenting cost data to a variety of audiences. Chart embellishments are one explored area of potential to increase the engagement and understanding of visualisations.