Miracle-dz/UniFAR-FTUs huggingface dataset:
Early changes in soil organic carbon following afforestation of former agricultural land

Afforestation of less productive, risky and degraded agricultural land is one of the methods which is recommended for practical agriculture to increase the carbon sequestration. In this study, we have attempted to determine the effect of afforestation of agricultural land (warm, mildly dry climatic region of the Czech Republic) on the soil organic carbon (Cox) concentrations in the mineral soil. Two soil types (Haplic Chernozem and Haplic Cambisol) were afforested. Both an indirect estimation (loss-on-ignition method) as well as chromsulfuric acid mixture oxidation were used to determine the organic carbon content in the soil samples and the methods were compared. In the case of the Haplic Chernozem, the Cox concentration at a depth of 0–10 cm after 1–3 years of afforestation with pedunculate oak or Scots pine significantly decreased (P < 0.01 and P < 0.004, respectively) with the stand age. Similar to the case of the Haplic Chernozem, the Cox concentration in the Haplic Cambisol also significantly decreased in the variants with Scots pine (P < 0.003) or a mixture of forest tree species (P < 0.006); no significant (P > 0.05) decrease was found in the case of a mixture of forest tree species on the Haplic Chernozem or with Douglas fir on the Haplic Cambisol. Significantly higher (P < 0.05) Cox concentrations were typically found in the case of 1-year-old stands compared to 2-year-old or 3-year-old stands. A higher Cox loss than the quantity of residues returned to the soils may be the reason the soil Cox concentration significantly (P < 0.00001 and P < 0.000001) decreased for the control agricultural plots (Haplic Chernozem and Haplic Cambisol). The carbon stock in the upper 10 cm of the 5-year-old stands was higher on the Haplic Chernozem and lower on the Haplic Cambisol compared to the control agricultural plots.
Modeling and assessment of adsorption ability of mussel powdered sunflower seed shell for Cr(VI) removal in batch and bed column systems

The valorization of non-hazardous natural and industrial/agriculture wastes has got a lot of attention from the scientific and industrial communities as clean and alternative low-cost materials for environmental remediation. The present work aims to evaluate the sorption ability of valorized sunflower seed shells (SSS) for the recovery of toxic Cr(VI) in batch and bed column systems. The physicochemical features of SSS powdered material were checked using different techniques including scanning electron microscopy, Fourier-transform infrared, thermogravimetric analysis, and differential scanning calorimetry to better understand the Cr(VI) sorption behavior. Briefly, it was found that the surface of SSS is well-functionalized with amides, amines, and carboxylic groups, which in turn may induce strong surface interactions with Cr(VI) ions in the solution. Sorption experiments showed a total recovery of Cr(VI) at 10 ppm, temperature of 298 K, and pH 1 within 10 min. The regeneration of saturated SSS adsorbent was carried out by simple distilled water washing in the bed column system. The results showed that the Cr(VI) can be released from the surface of SSS, for possible reuse of the material. However, the adsorption efficiency was diminished slightly during recycling which might be due to the partial saturation of SSS surface with chemically bonded Cr(VI). The sorption kinetics in batch and column systems were evaluated using five models, and the mechanism for Cr(VI) sorption on SSS was discussed.
Quantifying heterogeneous monsoonal melt on a debris-covered glacier in Nepal Himalaya using repeat uncrewed aerial system (UAS) photogrammetry

Abstract The ablation zones of debris-covered glaciers in Himalaya exhibit heterogeneous processes and melt patterns. Although sub-debris melt is measured at ablation stakes, the high variability of debris thickness necessitates distributed melt measurements at the glacier scale. Focusing on Annapurna III Glacier, we used uncrewed aerial system (UAS) photogrammetry to estimate total volume loss and slope-perpendicular glacier melt between May and November 2019 using flow-corrected point clouds. Results indicated the average elevation change was −1.10 ± 0.19 m, while the mean melt was −0.87 m w.e. , equating to a mean melt rate of −0.47 cm w.e. d−1. However, the spatial pattern was highly variable due to complex local processes necessitating future study over short intervals. The evaluation of specific areas showed the interplay of debris thickness variability, subseasonal debris redistribution, supraglacial channel reconfiguration and the imprint of relict ice cliffs in leading to contemporary melt rates. Ice cliffs had higher melt distances (mean −3.9 ± 0.19 m) compared to non-cliff areas (mean −0.75 ± 0.19 m) and were the predominant control on the spatial patterns of seasonal melt rates. Crucially, the definition of ice cliff areas from thinning data has a profound impact on derived melt rates and melt enhancement. Our study demonstrates the possibility and utility of deriving fully-distributed slope-perpendicular melt measurements.
PRBM_A_277850 61..75

1School of Management, Jiangsu University, Zhenjiang, Jiangsu, People’s Republic of China; 2Lahore Business School, University of Lahore, Lahore, Punjab, Pakistan Background: Corporate social responsibility (CSR) is emerging as a relevant subject in the business world and in the field of management research. Therefore, the current study incorporates classifications often used in organizational level CSR research that distinguish social responsibility relevant to its focus (internal and external), in proposing diverse routes that link various CSR practices (ie, internal and external) to employees’ choice of emotional labor strategy (ie, via perceived organizational support and perceived external prestige). Methods: Data were collected from front-line employees of banks operating in Pakistan. Due to the study’s focus on front-line employees, other personnel were excluded for data collection. We collected data through a self-administered questionnaire. The structural equation model (SEM) was employed on 376 valid responses using Smart-PLS3 to test the study hypotheses. Results: After the analysis, we found satisfactory results for the fitness of both measurement and satisfactory models. Moreover, the results strongly support our proposed theoretical framework, and all proposed hypotheses were accepted. Discussion: This study confirms that the perception of external prestige is a strong predictor of employees’ emotions and relevant behaviors. Moreover, this study discusses under the light of social exchange theory that perceived organizational support strongly predicts employees’ emotional labor, which diminishes the myth that prestige is the only factor to influence employees’ emotions in the workplace. Moreover, this study negates the findings of Anwar et al that perceived external prestige does not have a significant negative effect on surface acting. It provides an insight not only for managers and researchers but also for society, especially in an Eastern workplace setting like Pakistan’s banking sector.
DecenArch: a decentralized system for privacy-conscious Web archiving against censorship

The importance of Internet is today immeasurable and its impact on our lives continues to grow at an impressive pace. However, there are several important issues to be addressed. We observe a centralization of the service providers and continuous attacks on net neutrality, which increases the risk of censorship of the data stored on the Internet, either by deletion, modification or access denial. In addition to this, surveillance by public and private actors is a constant menace. It is therefore crucial to develop systems to resist against this censorship threat, while being attentive to privacy. We present DecenArch, a decentralized system for privacy-conscious webpages archiving. Independent servers retrieve the webpage pointed by a URL submitted by the user and agree a consensus on its content. The consensus protocol provides privacy for the parties, which reach a correct result even in presence of up to ⌊ n 3 ⌋ malicious adversaries, where n is the number of nodes in the system. The result of the consensus phase is then cryptographically verified by the servers and only if the entire process has been correctly executed, the webpage and its external resources are archived on a distributed immutable ledger. Anyone can then access the webpage stored on the ledger, thereby achieving censorship resistance. Empirical evaluation of our prototype implementation shows that DecenArch running on 16 hosts archives a real-world webpage in less than 10 minutes, while the retrieval of a saved page from the immutable ledger is almost instantaneous. The time complexity of DecenArch increases polynomially with the number of hosts and the size of the webpage being stored. We argue that the system is usable for real-world purposes and achieves a good level of security and privacy guarantees.
NDT_A_311473 2191..2198

1Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, People’s Republic of China; 2Department of Orthopaedics, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China Purpose: Depression is associated with an inflammatory immune response. There are minimal data regarding the association of inflammatory markers with depression in patients with spinal cord injury (SCI). We aimed to investigate the association of inflammatory markers with depression in middle-aged and elderly SCI patients. Methods: Data were obtained from the Midlife in the United States (MIDUS) study, a longitudinal study of a representative sample of the adult population. We analyzed the associations of serum levels of fibrinogen, interleukin-6, tumor necrosis factor-ɑ, and C-reactive protein with depressive symptoms. Results: The median participant age was 52.5 years; 44.9% of participants were men. Multivariate linear regression analyses showed that an increased serum fibrinogen level (Sβ = 0.114, p = 0.005) was associated with higher Centre for Epidemiological StudiesDepression (CES-D) scores after adjustment for age, sex, body mass index (BMI), ethnicity, education, marital status, smoking, alcohol use, exercise, perceived stress score, and cardiovascular disease (CVD). Multivariate logistic regression analysis showed that an increased serum fibrinogen level was independently associated with a history of depression (odds ratio [OR] = 1.240, 95% confidence interval [CI] = 1.103–1.997, p = 0.012) and depressive symptoms (OR = 1.884, 95% CI = 1.165–2.499, p < 0.001; CES-D score ≥ 16) after adjustment for confounding factors. Stratified analysis revealed that the association between serum fibrinogen level and depressive symptoms was affected by antidepressant use. Conclusion: Serum fibrinogen level had a significantly positive association with depressive symptoms in middle-aged and elderly patients with SCI. Future longitudinal cohort studies should evaluate the possible use of serum fibrinogen for diagnosis of depression in SCI patients.
An Investigation of the Role of Cohesive Identity Chains in Narrative Comprehension

This work examines how multimodal cohesive identity chains tied together in a visual narrative help viewers construct meaning while watching a film. The major contribution of this thesis is empirically-grounded research. The focus of the research was to study the effect of cohesive visual cues leading to the construction of cohesive identity chains on the narrative comprehension processes. For this purpose, data of 80 participants were collected and analyzed. The participants watched an extract of a film and were then presented with a questionnaire designed to assess their narrative comprehension. For this study, the visual cues and the temporal order in the extract were manipulated, which resulted in four experimental conditions. The results showed that the presence of the cohesive visual cues was essential for the participants’ narrative comprehension. The absence of the cohesive visual cues led to disrupted establishment of the primary identity chain and the goal of the main character in the episode. It also resulted in participants’ lack of confidence in responses even when those were correct. The temporal order in the selected extract was significant for the participants to establish the primary identity chain.
Differential access to dark markets and execution quality

We compare execution quality for dark pool trades across exchange-operated dark pools, where all trader types have equal access, and broker-operated dark pools, where brokers can restrict access to exclude certain traders, such as high frequency traders. Trades in broker dark pools have less information leakage and adverse selection than trades in exchange-operated dark pools. Differences in execution quality are concentrated in smaller trades. Broker dark pools that restrict access to high frequency traders entirely have less information leakage than those that do not. We conclude that the ability to segment order flow in dark pools can improve execution quality.
Participation in Setting Technology Standards and the Implied Cost of Equity

This paper empirically investigates the financial market’s reaction to firms’ participation in developing technology standards which is coordinated by Standard Setting Organizations (SSOs). We present the first evidence on the influence of SSO membership over a firm’s implied cost of equity capital - the discount rate applied by investors to its expected future cash flows. Our analysis utilizes a panel of 3,350 U.S. public firms and their memberships in 183 SSOs operating in Information and Communications Technologies (ICT) fields between 1996 and 2014. We find that SSO membership results in a statistically significant reduction in the cost of equity using exogenous variations from SSO closures and instrumental variables. This negative correlation is more pronounced for a firm’s first SSO membership, among members of the most influential SSOs as well as in ICT industries and certain technology domains. We empirically document the contingent role of three mechanisms – innovation intensity, market uncertainty and information environment – through which SSO membership can affect financial outcomes.
Public Information and Sequential Learning in a Bilateral Market

I study the effects of improved public information on equilibrium welfare and price dispersion, providing sufficient conditions for negative and positive effects. Public information affects welfare by reducing excessive (though rational) pessimism induced by sequential learning. Reduced pessimism results in fewer agents withdrawing from the market prematurely (compared with the full information benchmark), which increases own ex-post utility in expectation but may also cause a congestion externality. I show that either effect can dominate. Observed search duration on the short side of the market is an indicator of the welfare effects of public information. The context is a search, matching and bargaining market with uncertainty about the meeting probability on both sides. Fully rational, ex-ante identical participants gather private information endogenously through costly search. Full trade is the unique perfect Bayesian equilibrium under general conditions. Full trade implies learning terminates following a positive but not following a negative signal, which results in a declining belief path and in declining reservation prices during search. The results hold for any prior distribution where a more precise public signal slows learning.
Consistency of regularized spectral clustering in degree-corrected mixed membership model

Community detection in network analysis is an attractive research area recently. Here, under the degree-corrected mixed membership (DCMM) model, we propose an efficient approach called mixed regularized spectral clustering (Mixed-RSC for short) based on the regularized Laplacian matrix. Mixed-RSC is designed based on an ideal cone structure of the variant for the eigen-decomposition of the population regularized Laplacian matrix. We show that the algorithm is asymptotically consistent under mild conditions by providing error bounds for the inferred membership vector of each node. As a byproduct of our bound, we provide the theoretical optimal choice for the regularization parameter {\tau}. To demonstrate the performance of our method, we apply it with previous benchmark methods on both simulated and real-world networks. To our knowledge, this is the first work to design spectral clustering algorithm for mixed membership community detection problem under DCMM model based on the application of regularized Laplacian matrix.
Pressure-induced collapse of ferromagnetism in nickel

Transition metals, Fe, Co and Ni, are the canonical systems for studying the effect of external perturbations on ferromagnetism. Among these, Ni stands out as it undergoes no structural phase transition under pressure. Here we have investigated the long-debated issue of pressure-induced magnetisation drop in Ni from first-principles. Our calculations confirm an abrupt quenching of magnetisation at high pressures, not associated with any structural phase transition. We find that the pressure substantially enhances the crystal field splitting of Ni-3d orbitals, driving the system towards a new metallic phase violating the Stoner Criterion for ferromagnetic ordering. Analysing the charge populations in each spin channel, we show that the next nearest neighbour interactions play a crucial role in quenching ferromagnetic ordering in Ni and materials alike.
Graph-based Incident Aggregation for Large-Scale Online Service Systems

As online service systems continue to grow in terms of complexity and volume, how service incidents are managed will significantly impact company revenue and user trust. Due to the cascading effect, cloud failures often come with an overwhelming number of incidents from dependent services and devices. To pursue efficient incident management, related incidents should be quickly aggregated to narrow down the problem scope. To this end, in this paper, we propose GRLIA, an incident aggregation framework based on graph representation learning over the cascading graph of cloud failures. A representation vector is learned for each unique type of incident in an unsupervised and unified manner, which is able to simultaneously encode the topological and temporal correlations among incidents. Thus, it can be easily employed for online incident aggregation. In particular, to learn the correlations more accurately, we try to recover the complete scope of failures’ cascading impact by leveraging fine-grained system monitoring data, i.e., Key Performance Indicators (KPIs). The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that GRLIA is effective and outperforms existing methods. Furthermore, our framework has been successfully deployed in industrial practice.
The positive mass theorem and distance estimates in the spin setting

Let $\mathcal{E}$ be an asymptotically Euclidean end in an otherwise arbitrary complete and connected Riemannian spin manifold $(M,g)$. We show that if $\mathcal{E}$ has negative ADM-mass, then there exists a constant $R>0$, depending only on $\mathcal{E}$, such that $M$ must become incomplete or have a point of negative scalar curvature in the $R$-neighborhood around $\mathcal{E}$ in $M$. This gives a quantitative answer to Schoen and Yau's question on the positive mass theorem with arbitrary ends for spin manifolds. Similar results have recently been obtained by Lesourd, Unger and Yau without the spin condition in dimensions $\leq 7$ assuming Schwarzschild asymptotics on the end $\mathcal{E}$. We also derive explicit quantitative distance estimates in case the scalar curvature is uniformly positive in some region of the chosen end $\mathcal{E}$. Here we obtain refined constants reminiscent of Gromov's metric inequalities with scalar curvature.
Music Composition with Deep Learning: A Review

Generating a complex work of art such as a musical composition requires exhibiting true creativity that depends on a variety of factors that are related to the hierarchy of musical language. Music generation have been faced with Algorithmic methods and recently, with Deep Learning models that are being used in other fields such as Computer Vision. In this paper we want to put into context the existing relationships between AI-based music composition models and human musical composition and creativity processes. We give an overview of the recent Deep Learning models for music composition and we compare these models to the music composition process from a theoretical point of view. We have tried to answer some of the most relevant open questions for this task by analyzing the ability of current Deep Learning models to generate music with creativity or the similarity between AI and human composition processes, among others.
Towards a Methodology for Participant Selection in Software Engineering Experiments: A Vision of the Future

Background. Software Engineering (SE) researchers extensively perform experiments with human subjects. Well-defined samples are required to ensure external validity. Samples are selected purposely or by convenience, limiting the generalizability of results. Objective. We aim to depict the current status of participants selection in empirical SE, identifying the main threats and how they are mitigated. We draft a robust approach to participants' selection. Method. We reviewed existing participants' selection guidelines in SE, and performed a preliminary literature review to find out how participants' selection is conducted in SE in practice. Results. We outline a new selection methodology, by 1) defining the characteristics of the desired population, 2) locating possible sources of sampling available for researchers, and 3) identifying and reducing the "distance" between the selected sample and its corresponding population. Conclusion. We propose a roadmap to develop and empirically validate the selection methodology.
Task-aware Warping Factors in Mask-based Speech Enhancement

This paper proposes the use of two task-aware warping factors in mask-based speech enhancement (SE). One controls the balance between speech-maintenance and noise-removal in training phases, while the other controls the degree of enhancement applied to specific downstream tasks in testing phases. Our proposal is based on the observation that SE systems trained to improve speech quality often fail to improve other downstream tasks, such as automatic speaker verification (ASV) and automatic speech recognition (ASR), because they do not share the same objectives. It is easy to apply the proposed dual-warping factors approach to any mask-based SE method, and it allows a single SE base module to handle multiple tasks without task-dependent training. The effectiveness of our proposed approach has been confirmed on the SITW dataset for ASV evaluation and the LibriSpeech test-clean set for ASR and speech quality evaluations of 0-20dB. We show that different warping values are necessary in the testing phases for a single SE base module to achieve optimal performance w.r. t. the three tasks. With the use of task-aware warping factors, speech quality was improved by an 84.7% PESQ increase, while ASV had a 22.4% EER reduction, and ASR had a 52.2% WER reduction, on 0dB speech. The effectiveness of the task-aware warping factors were also cross-validated on VoxCeleb-1 test set for ASV and LibriSpeech dev-clean set for ASR and quality evaluations. The proposed method is highly effective and easy to apply in practice.