Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation Model of Low-Carbon Circular Economy Coupling Development in Forest Area Based on Radial Basis Neural Network

In this paper, we study the radial neural network algorithm for low-carbon circular economy in forest area, design a coupled development evaluation model, study its algorithmic ideas operation mode and the update formula obtained by standard algorithm, and finally optimize the RBF neural network by particle swarm algorithm. After an in-depth analysis of the particle swarm algorithm, an improved particle swarm algorithm is proposed to improve the search accuracy and capability of the algorithm by nonlinearly adjusting the inertia weights and introducing the average extreme value factor, in response to the problems of premature convergence and poor search capability that appear in the particle swarm algorithm. Through the analysis and evaluation of the interaction between industrial ecosystem and carbon emission, the main influencing factors of carbon emission are identified, and the size and magnitude of the influence of economic growth, industrial structure, energy intensity, and energy structure on carbon emission are determined; the current situation of the industrial ecological structure is evaluated, and the direction of optimization and adjustment of industrial economic structure, energy structure, and ecological structure is clarified. We construct a multidimensional multiconstraint multimodel industrial ecological structure optimization prediction model, set the development scenarios of economy and society, and optimize the prediction of low-carbon industrial ecological structure in forest areas; based on the simulation analysis of the prediction results, we propose the direction of industrial ecological structure adjustment and the path of industrial ecological system construction.
The Effect of Product-Harm Crises on the Financial Value of Firms under the Concept of Green Development

Product-harm crises can trigger product recalls or product discards, which is very likely to cause secondary pollution to the environment. Also, these crises may harm customers’ health and threaten firms’ survival. To foster low-carbon economy and green development in such complex systems, this paper studies the internal mechanism of the product crisis and its impact on the firm value. It proposes a two-stage model to avoid the endogeneity of product-harm crises. In the first stage, this paper assesses the effect of firms’ leverage on their capacity to produce higher quality products. In the second stage, this paper conducts the impact of these crises on stock prices. *en, it depicts the financial effects of product-harm crises over time, and analyzes the differences of such effects based on brand equity. Results show that book leverage can positively impact firms’ capacity to produce high-quality products. In addition, the market’s response to product-harm crises is significant at 1% level, and with the increase in severity, the market reaction is more prominent. Furthermore, its negative effect is persistent for a firm experiencing a severe crisis. Luckily, brand equity can mitigate this negative impact. *ese findings provide some ways to improve product performance and firm value in the green context.
Twin Subsequence Search in Time Series

We address the problem of subsequence search in time series using Chebyshev distance, to which we refer as twin subsequence search. We first show how existing time series indices can be extended to perform twin subsequence search. Then, we introduce TS-Index, a novel index tailored to this problem. Our experimental evaluation compares these approaches against real time series datasets, and demonstrates that TS-Index can retrieve twin subsequences much faster under various query conditions. This paper has been published in the 24th International Conference on Extending Database Technology (EDBT 2021).
A Training-Optimization-Based Method for Constructing Domain-Specific Sentiment Lexicon

Sentiment analysis has been widely used in text mining of social media to discover valuable information from user reviews. Sentiment lexicon is an essential tool for sentiment analysis. Recent research studies indicate that constructing sentiment lexicons for special domains can achieve better results in sentiment analysis. However, it is not easy to construct a sentiment lexicon for a specific domain because most current methods highly depend on general sentiment lexicons and complex linguistic rules. In this paper, the construction of sentiment lexicon is transformed into a training-optimization process. In our scheme, the accuracy of sentiment classification is used as the optimization objective. /e candidate sentiment lexicons are regarded as the individuals that need to be optimized. /en, two genetic algorithms are specially designed to adjust the values of sentiment words in lexicon. Finally, the best individual evolved in the presented genetic algorithms is selected as the sentiment lexicon. Our method only depends on some labelled texts and does not need any linguistic knowledge or prior knowledge. It provides a simple and easy way to construct a sentiment lexicon in a specific domain. Experiment results show that the proposed method has good flexibility and can generate high-quality sentiment lexicon in specific domains.
From Linear Programming Approach to Metaheuristic Approach: Scaling Techniques

The objective of this work is to propose ten efficient scaling techniques for the Wisconsin Diagnosis Breast Cancer (WDBC) dataset using the support vector machine (SVM). These scaling techniques are efficient for the linear programming approach. SVM with proposed scaling techniques was applied on the WDBC dataset. The scaling techniques are, namely, arithmetic mean, de Buchet for three cases , equilibration, geometric mean, IBM MPSX, and Lp-norm for three cases . The experimental results show that the equilibration scaling technique overcomes the benchmark normalization scaling technique used in many commercial solvers. Finally, the experimental results also show the effectiveness of the grid search technique which gets the optimal parameters (C and gamma) for the SVM classifier.
Decongestant: A Breath of Fresh Air for MongoDB Through Freshness-aware Reads

Many distributed databases deploy primary-copy asynchronous replication, and offer programmer control so reads can be directed to either the primary node (which is always up-to-date, but may be heavily loaded by all the writes) or the secondaries (which may be less loaded, but perhaps have stale data). One example is MongoDB, a popular document store that is both available as a cloud-hosted service and can be deployed on-premises. The state-of-practice is to express where the reads are routed directly in the code, at application development time, based on the program-mers’ imperfect expectations of what workload will be applied to the system and what hardware will be running the code. In this approach, the programmers’ choice may perform badly under some workload patterns which could arise during run-time. Furthermore, it might not be able to utilize the given resources to their full potential – meaning database customers pay more money than needed. In this paper, we present Decongestant: a system which will automatically and dynamically, as the application is running, choose where to direct reads, sending enough reads to secon-daries when this will reduce load on a congested primary and boost the performance of the database as a whole, but without exceeding the maximum data staleness that the clients are willing to accept. A central insight is to use measured latency of read operations on primary and secondaries to determine whether the primary is congested. In an experimental evaluation, we demonstrate our system adapts well to dynamically changing workloads, obtaining performance benefits when they can arise from use of the secondaries, while ensuring that returned values are fresh enough given client requirements. Our approach is decentralised and can be used by both cloud-consumers and on-premises users: it uses only client observations and the limited diagnostic data provided by the database to its clients.
Toward Pleomorphic Reconfigurable Robots for Optimum Coverage

Buildings are constructed for accommodating living and industrial needs. Floor cleaning robots have been developed to cater to the demand of these buildings. Area coverage and coverage time are crucial performance factors of a floor cleaning robot. Reconfigurable tiling robots have been introduced over fixed shape robots to improve area coverage in floor cleaning applications compared to robots with fixed morphologies. However, area coverage and coverage time of a tiling robot compromised one another. *is study proposes a novel concept that considers the ability of a tiling robot to configure both its morphology and size according to the environment. *is concept is inspired by the pleomorphism that could be seen in bacteria. In this regard, P-hTetro, a pleomorphic tiling robot that can reconfigure its morphology and size, is considered. A novel coverage strategy for realizing the size reconfiguration is also proposed. According to this strategy, the robot covers obstacle-free areas with its maximum size, while an obstacle cluster is covered after shrinking to an optimum size. *e optimum size for reconfiguration is determined by the genetic algorithm based on the arrangement of the environment. *e performance and behavior of the proposed P-hTetro have been compared against that of an existing tiling robot which has a fixed size. According to the statistical outcomes, a tiling robot with the ability to reconfigure its size can significantly improve the performance in the aspects of area coverage and coverage time compared to a tiling robot with no ability to reconfigure its size.
STIs during the first and second wave of COVID-19 in Denmark

During the first wave of COVID-19, several European studies reported an unchanged incidence of STIs in the first half of 2020. Only a Spanish study found a decreased number. Data from the last part of 2020 are lacking, and due to long incubation periods of STIs, followup is important. In Denmark, the first lockdown began in March 2020 with, among other things, closed nightlife and a public gatherings limit of 10. Restrictions reduced the incidence of COVID-19 and were loosened over summer so that bars could open and gatherings could include 100. The second wave started in September and culminated in December 2020. In response, nightlife was closed and gathering limit reduced to 10 during the fall and winter. As COVID-19 cause more harm on the elderly, one might expect them to follow social distancing recommendations more closely leading to a shift in age distribution among patients with STIs. Bonato et al found an unaffected mean age of syphilis patients during the first wave. None of the other studies examined age distribution. The present study investigated the numbers of patients diagnosed with gonorrhoea or syphilis during the first and second wave of COVID-19 in Denmark compared with the same period the three previous years. Additionally, the study investigated any possible change in the proportion of young and older infected. Laboratoryconfirmed cases of gonorrhoea and syphilis in Denmark are published by Denmark’s national surveillance database for infectious diseases. Monthly numbers of gonorrhoea and syphilis cases from the beginning of 2017 to the end of 2020 were extracted from this freely available database. Subsequently, yearly numbers of patients older than 54 years were obtained. A total of 2027 patients were diagnosed with gonorrhoea from March 2020 to December 2020. That was 141 patients more than during the same period the year before. Three hundred and fiftyeight patients were diagnosed with syphilis from March 2020 to December 2020. That was 47 patients more than during the same period the year before. Numbers of gonorrhoea and syphilis patients have grown over the past 4 years, and numbers in 2020 were an extension hereof (figure 1A). For both gonorrhoea and syphilis, the proportion of patients over the age of 54 years was constant from 2017 to 2020 (figure 1A). The monthly number of gonorrhoea and syphilis patients were comparable in 2019 and 2020 (figure 1). COVID-19 restrictions had no impact on the incidence or proportion of young and older infected with gonorrhoea or syphilis. Results from this study with longer followup is in accordance with earlier reports from Europe. As for the rest of the world, in the USA, STIs incidences during the first half of 2020 has been unchanged, while in Taiwan, an increase has been seen. Lebanon had a major reduction in STIs testing rates during the COVID-19 outbreak, reminding us that the numbers of diagnosed STIs are dependent on behaviour and on access to medical services. In conclusion, stable management of STIs during the continued COVID-19 pandemic is required.
Hepatocellular carcinoma recurrence after direct-acting antiviral therapy: an individual patient data meta-analysis

Objective The benefit of direct-acting antivirals (DAAs) against HCV following successful treatment of hepatocellular carcinoma (HCC) remains controversial. This meta-analysis of individual patient data assessed HCC recurrence risk following DAA administration. Design We pooled the data of 977 consecutive patients from 21 studies of HCV-related cirrhosis and HCC, who achieved complete radiological response after surgical/locoregional treatments and received DAAs (DAA group). Recurrence or death risk was expressed as HCC recurrence or death per 100 person-years (100PY). Propensity score-matched patients from the ITA.LI. CA. cohort (n=328) served as DAA-unexposed controls (no-DAA group). Risk factors for HCC recurrence were identified using random-effects Poisson. Results Recurrence rate and death risk per 100PY in DAA-treated patients were 20 (95% CI 13.9 to 29.8, I2=74.6%) and 5.7 (2.5 to 15.3, I2=54.3), respectively. Predictive factors for recurrence were alpha-fetoprotein logarithm (relative risk (RR)=1.11, 95% CI 1.03 to 1.19; p=0.01, per 1 log of ng/mL), HCC recurrence history pre-DAA initiation (RR=1.11, 95% CI 1.07 to 1.16; p<0.001), performance status (2 vs 0, RR=4.35, 95% CI 1.54 to 11.11; 2 vs 1, RR=3.7, 95% CI 1.3 to 11.11; p=0.01) and tumour burden pre-HCC treatment (multifocal vs solitary nodule, RR=1.75, 95% CI 1.25 to 2.43; p<0.001). No significant difference was observed in RR between the DAA-exposed and DAA-unexposed groups in propensity score-matched patients (RR=0.64, 95% CI 0.37 to 1.1; p=0.1). Conclusion Effects of DAA exposure on HCC recurrence risk remain inconclusive. Active clinical and radiological follow-up of patients with HCC after HCV eradication with DAA is justified.
Gretch

Data-dependent memory accesses (DDAs) pose an important challenge for high-performance graph analytics (GA). This is because such memory accesses do not exhibit enough temporal and spatial locality resulting in low cache performance. Prior efforts that focused on improving the performance of DDAs for GA are not applicable across various GA frameworks. This is because (1) they only focus on one particular graph representation, and (2) they require workload changes to communicate specific information to the hardware for their effective operation. In this work, we propose a hardware-only solution to improving the performance of DDAs for GA across multiple GA frameworks. We present a hardware prefetcher for GA called Gretch, that addresses the above limitations. An important observation we make is that identifying certain DDAs without hardware-software communication is sensitive to the instruction scheduling. A key contribution of this work is a hardware mechanism that activates Gretch to identify DDAs when using either in-order or out-of-order instruction scheduling. Our evaluation shows that Gretch provides an average speedup of 38% over no prefetching, 25% over conventional stride prefetcher, and outperforms prior DDAs prefetchers by 22% with only 1% increase in power consumption when executed on different GA workloads and frameworks.
van der Waals potentials from the infrared spectra of rare gas–HF complexes

High‐resolution infrared spectra of the Ar–HF, Kr–HF, and Xe–HF van der Waals molecules have been recorded in the vicinity of the H–F stretching fundamentals, ν1, under thermal equilibrium conditions at T≂211 K with a tunable difference‐frequency laser. Rotational structure has been observed up to or approaching rotational predissociation, permitting us to model the effective radial van der Waals potentials for these complexes. These potentials provide good estimates for the binding energies, D0, and the van der Waals stretching frequencies, ν3, in the ground (v1=0) and excited (v1=1) states of the molecules. For v1=0 in Ar–HF, Kr–HF, and Xe–HF, we find D0=102, 133, and 181 cm−1 and ν3=39.2, 41.1, and 43.4 cm−1, respectively. The ν3 modes characterized by the model potentials aid in the assignment of the ν1+ν3−ν3 hot bands observed in our spectra. The band centers for the ν1 fundamentals are all down shifted in frequency from the isolated HF monomer by Δν=−9.654, −17.518, and −29.185 cm−1 for the Ar, Kr, .
Effect of Chemical Oxidation on the Structure of Single-Walled Carbon Nanotubes

In the present study, we report the systematic investigation of the effect of chemical oxidation on the structure of single-walled carbon nanotubes (SWNTs) by using different oxidants. The oxidation procedure was characterized by using infrared spectroscopy and transmission electron microscopy (TEM). The SWNTs were produced by chemical vapor deposition (CVD) and oxidized with three kinds of oxidants: (1) nitric acid (2.6 M), (2) a mixture of concentrated sulfuric acid (98 wt %) and concentrated nitric acid (16 M) (v/v = 3/1) and (3) KMnO4. The results reveal that the different functional groups can be introduced when the SWNTs are treated with different oxidants. Refluxing in dilute nitric acid can be considered as a mild oxidation for SWNTs, introducing the carboxylic acid groups only at those initial defects that already exist. The abundance of the carboxylic acid groups generated with this oxidant remained constant along with the treating time. In contrast, sonication of SWNTs in H2SO4/HNO3 increased .
Antibacterial Drug Release Electrochemically Stimulated by the Presence of Bacterial Cells – Theranostic Approach

A modified electrode with bioaffinity to E. coli bacterial cells was used to detect bacteria presence on its surface and to produce an electrical signal triggering antibacterial drug release from another coupled electrode. The Sense-and-Treat system was realized in two different versions and suggested as the first step to future theranostic applications where bacteria presence results in automatic antibacterial treatment.
FT-Raman spectroscopy study for skin cancer diagnosis

Early detection of cancer is still a great challenge in clinical oncology. Recently, Raman spectroscopy has been used for skin lesion detection. FT-Raman spectroscopy is a modern analytical tool and its use for cancer diagnosis will lead to several advantages for the patient as, for example, real time and less invasive diagnosis. The primary objective of this work was to use FT-Raman spectroscopy to detect spectral changes between benign and malignant (basal cell carcinoma - BCC) skin tissues. Those spectral changes can provide important information about the biochemical alterations between these two types of tissues. We have analyzed by FT-Raman eight set of samples histopathologically diagnosed as BCC and made a comparison with five set of samples diagnosed as benign tissue. We have found that the main spectral differences between these samples were in the shift region of 1220-1300 cm −1 and 1640-1680 cm −1 . The vibration bands in these regions correspond to the amide III and to the amide I vibrations, respectively. Principal components analysis applied over all 13 samples could identify tissue type with 100% of sensitivity and specificity.
Coagulation Efficacy of Moringa Oleifera Seed Extract Compared to Alum for Removal of Turbidity and E. coli in Three Different Water Sources.

Concerns over exposure to drinking water contaminants by rural and peri-urban dwellers in Africa, resultant adverse effects on human health, andhigh cost of water treatment facilities and chemicals have prompted several studies on the use of natural coagulants like Moringa Oleifera. This study was conductedto assess the efficacy of powder extracted from mature-dried Moringa Oleifera seeds for treatment of contaminated water. Moringa seeds were shelled, oven-dried and milled, after which the oil content was extracted with a solvent (n-hexane) in an electro thermal soxhlet apparatus and the residue was used as water coagulant. Representative water samples from stream, pond and well were collected and subjected to purification studies using seed extract of Moringa Oleifera and well processed certified Aluminum Sulphate. The efficiencies of the two coagulants were evaluated using a standard jar test. At optimum dosages of 2.5, 4.5 and 6.0 ml of M.O. seeds extract, turbidity removal efficiencies were 90.4%, 95.6% and 96.7% for the well, stream and pond water samples respectively. The respective values for alum at optimum dosages of 2.0, 3.5 and 4.0ml were 100%, 99.9% and 99.9%. M.O. and Alum reduced the E.Coli contents of the stream water to 33 CFU/100ml and 76 CFU/100ml respectively. E.coli removal efficiencies of M.O. and Alum for the pond water were 86% and 44% respectively. For the well water sample, the removal efficiencies recorded for Moringa and Alum were 90% and 52% respectively.The results show that M.O. seed possesses some antimicrobial properties and good coagulating substance that is comparable to commercial alum in turbidity removal. The natural coagulant is biodegradable, environmentally friendly and non-toxic, thus, making it a potentially viable substitute to alum in addressing the challenges facing potable water supplyespecially in rural and peri-urban areas of developing countries.