Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges of API Documentation from a Provider Perspective and Best Practices for Examples in Public Web API Documentation

: Developers frequently have to learn new Web APIs provided by other teams or organizations. Documentation, especially code examples, supports learning and inﬂuences the consumers’ perception of an API. Nevertheless, documentation repeatedly fails to address consumers’ information needs. Therefore, we identify four major challenges of creating and maintaining public Web API documentation from a provider perspective which are unknown customer needs, the difﬁculty of balancing the coverage of varying information needs and keeping documentation concise, the high effort of creating and maintaining documentation, and missing internal guidance and governance for creating API documentation. In addition, we derive 46 best practices candidates for code examples as part of Web API documentation from literature and 13 expert interviews. Moreover, we evaluate a subset of eight of these candidates in the context of the Web API documentation for a public GraphQL API in a case study with 12 participants. As a result, we validate the analyzed eight best practices candidates to be best practices for public Web API documentation.
Dancing with Douglas‐fir: Determinism dominates fungal community assembly processes

Fungal communities can influence the productivity, composition and survival of trees through cycling nutrients, providing resources and altering pathogens. Thus, shifts in fungal communities could impact forests by altering interactions between trees and their environments. Fungal community composition may be shaped by stochastic and deterministic processes such as dispersal limitation, environmental filtering and partner specificity between trees and fungi. For tree species with large geographical ranges, we expect fungal assembly processes to change with environmental variation across the range of the tree partner. Due to specificity between trees and symbiotic fungi, we expect deterministic to outweigh stochastic processes in root compared with soil fungi. As some tree species have exceptional longevity, we also expected tree age to influence fungal community assembly. We surveyed fungi in four stands of Pseudotsuga menziesii with tree ages up to 800 years along an 1,800 km transect. We sampled roots and soil around 12 P. menziesii in each stand, aged the trees, and sequenced fungal rDNA to determine composition and richness from which we calculated the relative role of deterministic and stochastic assembly processes. We used null models to evaluate the relative importance of deterministic variable and homogenizing selection, and stochastic dispersal limitation, drift and homogenizing dispersal in fungal community assembly. We detected 7,280 amplicon sequence variants with 5,270 associated with soil, 3,887 with roots and 1,877 found across both roots and soils. Deterministic processes dominated root and soil fungal communities at all sites except one where stochastic processes (i.e. dispersal limitation and drift) controlled root fungi. Despite the dominance of determinism in fungal community assembly, the proportion of processes differed by site. Assembly processes did not vary with tree age. Synthesis. Taken together, we suggest that the local environment, water limitation and partner‐preference between trees and their associated fungi, influence fungal community composition across the range of P. menziesii. We conclude that while fungal communities occurring near P. menziesii are dominated by homogenizing selection, the role of neutral processes still has a minor influence on community assembly and may be important in spatially isolated communities and those with strong gradients of fungal diversity.
Characterization of Paper-Like Material Prepared from Chitosan/Graphene Oxide Composite

: Chitosan (CTS) is considered to be a common biomacromolecule/poly-cationic compound containing the potential functional groups that can be utilized as a feedstock for novel materials. In this study, CTS/graphene oxide (CTS/GO, CG) mixtures were prepared at different conditions to confirm a suitable hydrogel formation, then applied to produce paper-like materials with various thickness via a simple casting method. As a result, the morphological structure finally yielded the paper-like materials (CG2 papers with varying numbers of casting times) with the layer-by-layer structures instead of the tightly-sticky paper-like structure (GO paper). Based on the possible interactions between the CTS molecules and GO nanosheets, the CG mixtures could also be determined by FTIR and Raman analysis; concomitantly, their thermal properties reach higher than that of the pure GO. Notably, the strong interactions and compatibility of the CTS molecules and GO nanosheets revealed good dispersion and interfacial adhesion, leading to significantly enhancing the mechanical properties of the CG2 paper-like materials with increasing number of casting times or compared to GO paper. Therefore, the CG2 paper-like materials with the various numbers of casting times fabricated in the present study can expose new approaches for the design and application of future foil/paper-like materials, as well as the desired thickness of these foil/paper-like materials can be controlled easily.
Influence of Polymorphism on the Electrochemical Behavior of Dilithium (2,3-Dilithium-oxy)-terephthalate vs. Li

Organic electrode materials offer obvious opportunities to promote cost-effective and environmentally friendly rechargeable batteries. Over the last decade, tremendous progress has been made thanks to the use of molecular engineering focused on the tailoring of redox-active organic moieties. However, the electrochemical performance of organic host structures relies also on the crystal packing, like the inorganic counterparts, which calls for further efforts in terms of crystal chemistry to make a robust redox-active organic center electrochemically efficient in the solid state. Following our ongoing research aiming at elaborating lithiated organic cathode materials, we report herein on the impact of polymorphism on the electrochemical behavior of dilithium (2,3-dilithium-oxy-)terephthalate vs. Li. Having isolated dilithium (3-hydroxy-2-lithium-oxy)terephthalate through an incomplete acid-base neutralization reaction, its subsequent thermally induced decarboxylation mechanism led to the formation of a new polymorph of dilithium (2,3-dilithium-oxy-)terephthalate referred to as Li4-o-DHT (β-phase). This new phase is able to operate at 3.1 V vs. Li+/Li, which corresponds to a positive potential shift of +250 mV compared to the other polymorph formerly reported. Nevertheless, the overall electrochemical process characterized by a sluggish biphasic transition is impeded by a large polarization value limiting the recovered capacity upon cycling.
Planning Water Resources Systems under Uncertainty Using an Interval-Fuzzy De Novo Programming Method

This study presents an interval-fuzzy De Novo programming (IFDNP) method for planning water-resources-management systems under uncertainty. IFDNP is derived by incorporating the concepts of interval parameters and fuzzy sets within a De Novo programming framework. IFDNP has the advantages in constructing optimal system design through introducing the flexibility into the available resources in the model’s constraints. Moreover, IFDNP allows the decision makers to achieve a metaoptimal system performance and improve the performance of compromise solutions, and it is effective for dealing with the system design problems involving multiple objectives and multiple uncertainties. The IFDNP is then applied to a case study of designing an inexact optimal system with budget limit for water resources management and planning. Various scenarios that are associated with different levels of economic implication consequences and water allocation patterns under uncertainty are analyzed. Results can help decision makers to evaluate alternatives of system designs and to determine which of these designs can most efficiently achieve the desired economic objective constrained by limited resources.
Why should hotels turn green? Exploring emergent sustainable behaviors on lake Garda

Although sustainability has been widely addressed within tourism research in the last decades, the debate on this complex and multidimensional topic is still far from its conclusion and current literature is moving the focus from theory building to theory testing. Since pressures on natural resources preservation are continuously increasing, the aim of this study is to investigate which are the emergent environmental behaviors adopted by hotel companies and the motivations that lie behind their implementation. The empirical research is conducted through semi-structured interviews to hotel managers operating in a tourist destination on lake Garda (Italy). According to previous literature, we argue that perceptions about sustainability are still not univocal among hoteliers, and that the motivations behind sustainable behaviors are more than often a compromise between two opposing forces: institutional and instrumental, i.e. legitimacy and profitability. Moreover, since environmental tools are not yet largely adopted in the hospitality industry, we also presume that sustainability is an innovative practice from which it is still possible to derive social and economic advantages. The present study offers some useful contributions from a theoretical perspective, since it adds to the current literature on sustainability by originally connecting this compelling topic to the rooted neo-institutional theoretical framework, but also some practical implications for all the subjects involved in the hospitality industry by enhancing the awareness on this critical issue.
Monitoring, Diagnosis, and Fault-Tolerant Control of Wind Turbines

Governments across the globe are funding renewable energy initiatives like wind energy to diversify energy resources and promote a greater environmental responsibility. Such an opportunity requires state-of-the-art technologies to realize the required levels of efficiency, reliability, and availability in modern wind turbines. The key enabling technologies for ensuring reliable and efficient operation of modern wind turbines include advanced condition monitoring and diagnosis together with fault-tolerant and efficiency/optimal control. Application of the mentioned technologies in wind turbines constitutes a quite active and, in many aspects, interdisciplinary investigation area that ensures a guaranteed increasing future market for wind energy. In particular, this thesis aims to design and develop novel condition monitoring, diagnosis and fault-tolerant control schemes with application to wind turbines at both individual wind turbine and entire wind farm (i.e., a group of wind turbines) levels. Therefore, the research of the thesis provides advanced levels of monitoring, diagnosis and fault tolerance capabilities to wind turbines in order to ensure their efficient and reliable performance under both fault-free and faulty conditions. Finally, the proposed schemes and strategies are verified by a series of simulations on well-known wind turbine and wind farm benchmark models in the presence of wind turbulences, measurement noises, and different realistic fault scenarios.
A Survey on Memory Subsystems for Deep Neural Network Accelerators

From self-driving cars to detecting cancer, the applications of modern artificial intelligence (AI) rely primarily on deep neural networks (DNNs). Given raw sensory data, DNNs are able to extract high-level features after the network has been trained using statistical learning. However, due to the massive amounts of parallel processing in computations, the memory wall largely affects the performance. Thus, a review of the different memory architectures applied in DNN accelerators would prove beneficial. While the existing surveys only address DNN accelerators in general, this paper investigates novel advancements in efficient memory organizations and design methodologies in the DNN accelerator. First, an overview of the various memory architectures used in DNN accelerators will be provided, followed by a discussion of memory organizations on non-ASIC DNN accelerators. Furthermore, flexible memory systems incorporating an adaptable DNN computation will be explored. Lastly, an analysis of emerging memory technologies will be conducted. The reader, through this article, will: 1—gain the ability to analyze various proposed memory architectures; 2—discern various DNN accelerators with different memory designs; 3—become familiar with the trade-offs associated with memory organizations; and 4—become familiar with proposed new memory systems for modern DNN accelerators to solve the memory wall and other mentioned current issues.
SEGRESS: Software Engineering Guidelines for REporting Secondary Studies

Context: Several tertiary studies have criticized the reporting of software engineering secondary studies. Objective: Our objective is to identify guidelines for reporting software engineering (SE) secondary studies which would address problems observed in the reporting of software engineering systematic reviews (SRs). Method: We review the criticisms of SE secondary studies and identify the major areas of concern. We assess the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement as a possible solution to the need for SR reporting guidelines, based on its status as the reporting guideline recommended by the Cochrane Collaboration whose SR guidelines were a major input to the guidelines developed for SE. We report its advantages and limitations in the context of SE secondary studies. We also assess reporting guidelines for mapping studies and qualitative reviews, and compare their structure and content with that of PRISMA 2020. Results: Previous tertiary studies confirm that reports of secondary studies are of variable quality. However, ad hoc recommendations that amend reporting standards may result in unnecessary duplication of text. We confirm that the PRISMA 2020 statement addresses SE reporting problems, but is mainly oriented to quantitative reviews, mixed-methods reviews and meta-analyses. However, we show that the PRISMA 2020 item definitions can be extended to cover the information needed to report mapping studies and qualitative reviews. Conclusions: In this paper and its Supplementary Material, we present and illustrate an integrated set of guidelines called SEGRESS (Software Engineering Guidelines for REporting Secondary Studies), suitable for quantitative systematic reviews (building upon PRISMA 2020), mapping studies (PRISMA-ScR), and qualitative reviews (ENTREQ and RAMESES), that addresses reporting problems found in current SE SRs.
Evaluation of Non-HDL-C and Calculated Lipid Profile Ratios among Pre-Diabetic Individuals

Introduction: The worldwide prevalence of diabetes mellitus is rapidly increasing. The major concern is the complexity and mortality that are secondary to the complications of diabetes mellitus. One among the complications is dyslipidaemia which leads to atherosclerotic diseases. Pre-diabetes is a mid-state between non-diabetes and diabetes mellitus. They have a greater chance of progressing into the disease. Dyslipidaemia, a major aetiological factor for atherosclerosis, is also noted in pre-diabetic stage, report studies. This study was a cross-sectional study involving 26 non-diabetic controls, 26 prediabetic and 26 diabetic cases. The participants were of 18-45 years of age. The data was collected over a period of three months. The groups were classified based on their HbA1c concentration. The blood samples were analysed for fasting lipid profile and the parameters, Non-HDL-C, TC/HDL-C and LDL-C/HDL-C obtained by using respective methods. The obtained lipid profile parameters, Non-HDL-C, TC/HDL-Cand LDL-C/HDL-C were found to be increased in pre-diabetic subjects and diabetic subjects when compared to the healthy non-diabetic controls. The parameters also showed a positive correlation and was statistically highly significant among the three groups. There was a positive association observed among the groups for the calculated lipid profile parameters, Non-HDL-C, TC/HDL-C and LDL-C/HDL-C and were found to be directly proportional to HbA1c.
Non-linear modal analysis of transient interaction behaviours in SEPIC DC–DC converters

This study investigates the transient interaction behaviours in SEPIC DC–DC converters by using non-linear modal series method. Based on the derived non-linear model, the analytical approximate solutions are obtained to characterise the transient behaviours of the converters. Then, fundamental modal analysis is performed to identify the correlation among system parameters, fundamental modes and state variables so that system key parameters are subsequently acquired. In addition, the second-order interaction indices are evaluated in order that the underlying mechanism of non-linear interaction behaviours is uncovered. Finally, theoretical analysis is verified by circuit experiments. These results are beneficial to the improvement of the transient behaviours and the system reliability.
Management of Breast Abscess during Breastfeeding

(1) Background: Breast abscess (BA) is a condition leading in the majority of cases to breastfeeding interruption. Abscesses are commonly treated with antibiotics, needle aspiration or incision and drainage (I&D), but there is still no consensus on the optimal treatment. Since there are no well-defined clinical guidelines for abscess management, we conducted a retrospective, observational study with the aim of assessing ultrasound (US)-guided management of BA without surgery, regardless of the BA size. The secondary objective was the microbiologic characterization and, in particular, the S. aureus methicillin resistance identification. (2) Methods: our population included 64 breastfeeding mothers with diagnosis of BA. For every patient, data about maternal, perinatal and breastfeeding features were collected. All patients underwent office US scans and 40 out of 64 required a more detailed breast diagnostic ultrasound performed by a radiologist. In all cases, samples of milk or abscess material were microbiologically tested. All patients received oral antibiotic treatment. We performed needle aspiration, when feasible, even on abscesses greater than 5 cm. (3) Results: most of the women developed BA during the first 100 days (68.8% during the first 60 days) after delivery and 13 needed hospitalization. Four abscesses were bilateral and 16 had a US major diameter greater than 5 cm. All patients were treated with antibiotic therapy according to our clinical protocol and 71.9% (46/64) underwent fine needle aspiration. None of them required I&D. The average duration of breastfeeding was 5 months (IR 2; 9.5) and 40.6% of women with BA continued to breastfeed for more than 6 months. Only 21 mothers interrupted breastfeeding before 3 months. (4) Conclusions: our observational data suggest, regardless of the size and the clinical features of the BA, a conservative approach with antibiotic therapy targeted at the Methicillin-Resistant Staphilococcus aureus (MRSA) identified and needle aspiration, if feasible. In our experience, treatment with needle aspiration is a cost- effective method. Unlike drainage, it is an outpatient procedure, easily repeatable, with no cosmetic damage. In addition, it has lower risk of recurrences since, differently from surgical incision, it does not cause interruption of the ducts. Moreover, needle aspiration is less painful, does not require the separation of the mother-child dyad and allows for a quicker, if not immediate, return to breastfeeding.
CANFIS: A Chaos Adaptive Neural Fuzzy Inference System for Workload Prediction in the Cloud

As cloud-based applications become increasingly solicited by companies and individuals, the competition between cloud providers that offer cloud services keeps increasing. To win this competition, cloud providers must provide sufficient computing resources that will satisfy users’ demand for every request. Workload prediction has been investigated extensively to resolve this issue using various techniques. This paper presents a workload prediction method called CANFIS, which combines the Savitzky-Golay (SG) filter and Chaotic time series analysis with the Adaptive Neural Fuzzy Inference System (ANFIS) to make predictions of cloud workloads. The SG filter is used to clean data from noise and outliers, and chaotic analysis is used to investigate the chaotic nature of workload and to build the improved ANFIS model. The proposed method is evaluated using real workload traces from web applications (i.e., Wikipedia and NASA Kennedy traces) and cluster applications (i.e., CPU and Memory of Google cluster). Experimental results show that the proposed CANFIS model can improve prediction accuracy compared to existing techniques, including simple ANFIS, Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM), Long Short Term Memory (LSTM), and Neural Networks based methods. A statistical analysis is also performed using the Friedman test, along with Finner post hoc analysis to verify the efficiency of the proposed prediction model.
ADC-DL: Communication-efficient distributed learning with hierarchical clustering and adaptive dataset condensation

The rapid growth of modern mobile devices leads to a large number of distributed data, which is extremely valuable for learning models. Unfortunately, model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns, hindering artificial intelligence from empowering mobile devices. Moreover, these data are not identically and independently distributed (Non-IID) caused by their different context, which will deteriorate the performance of the model. To address these issues, we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation, named ADC-DL, which learns a shared model by collecting the synthetic samples generated on each device. To tackle the heterogeneity of data distribution, we propose an entropy topsis comprehensive tiering model for hierarchical clustering, which distinguishes clients in terms of their data characteristics. Subsequently, synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation. The procedure of dataset condensation can be adjusted adaptively according to the tier of the client. Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.