Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of different drinking water treatments for natural organic matter removal

In the past decades, natural organic matter (NOM), which is a complex heterogeneous mixture of organic materials that are commonly present in all surface, ground and soil waters, has had an adverse effect on drinking water treatment. The existence of NOM results in many problems in drinking water treatment processes, and the properties and amount of NOM can significantly affect the efficiency of these processes. NOM not only influences the water quality with respect to taste, color and odor problems, but it also reacts with disinfectants, increasing the amount of disinfection by-products. NOM can be removed from drinking water via several treatment processes, but different drinking water treatment processes have diverse influences on NOM removal and the safety of the drinking water. Several treatment options, including coagulation, adsorption, oxidation, membrane and biological treatment, have been widely used in drinking water purification processes. Therefore, it is of great importance to be able to study the influence of different treatment processes on NOM in raw waters. The present review focuses on the methods, including coagulation, adsorption, oxidation, membrane, biological treatment processes and the combination of different treatment processes, which are used for removing NOM from drinking water.
Experimental and modeling investigations of the behaviors of syntactic foam sandwich panels with lattice webs under crushing loads

Abstract The composite sandwich structures with foam core and fiber-reinforced polymer skin are prone to damage under local impact. The mechanical behavior of sandwich panels (glass fiber-reinforced polymer [GFRP] skin reinforced with lattice webs and syntactic foams core) is studied under crushing load. The crushing behavior, failure modes, and energy absorption are correlated with the number of GFRP layers in facesheets and webs, fiber volume fractions of facesheets in both longitudinal and transverse directions, and density and thickness of syntactic foam. The test results revealed that increasing the number of FRP layers of lattice webs was an effective way to enhance the energy absorption of sandwich panels without remarkable increase in the peak load. Moreover, a three-dimensional finite-element (FE) model was developed to simulate the mechanical behavior of the syntactic foam sandwich panels, and the numerical results were compared with the experimental results. Then, the verified FE model was applied to conduct extensive parametric studies. Finally, based on experimental and numerical results, the optimal design of syntactic foam sandwich structures as energy absorption members was obtained. This study provides theoretical basis and design reference of a novel syntactic foam sandwich structure for applications in bridge decks, ship decks, carriages, airframes, wall panels, anticollision guard rails and bumpers, and railway sleepers. Graphical abstract
Treatment modality, diabetic control and blood homeostasis in type 2 diabetes mellitus patients in Basra

Abstract This study is aimed at investigating the relationship between treatment modality, glycemic control and blood homeostasis as represented by prothrombin time (PT) and activated thromboplastin time (APTT) in T2DM patients. Sixty-four type 2 diabetic patients (40 males and 24 females) on metformin or insulin or both for not less than six months of ages between 20 and 75 years were selected during their visit to Diabetes Endocrine and Metabolism Centers in two General Hospitals in Basra. Socioeconomic characteristics and treatment plans were recorded. Glycated haemoglobin (HbA1c), lipid profiles and hematological parameters measured in blood samples were taken. Lower mean HbA1c (p = 0.0383) was found in patients on metformin alone, higher percentage of hemoglobin was found in patients on insulin treatment, and higher levels of LDL (P = 0.0018) and HDL (P = 0.0241) in patients on metformin and insulin. Treatment manner had no effect on mean PT or APTT, however, there was a significant inverse correlation of PT with LDL (P = 0.0042), and a direct correlation of APTT with HbA1c (p = 0.0209) and an inverse correlation of APTT with platelets count (P = 0.0324) in patients on insulin treatment. In addition, there was a significant direct correlation of APTT with triglycerides (TG) (P = 0.0069) in patients on metformin treatment. In conclusion, lower mean of HbA1c was found only in patients receiving metformin drug alone, higher Hb percentage were found in patients treated with insulin alone, higher LDL and higher HDL levels were found in patients treated with both metformin and insulin for at least six months. Treatment manner had no effect on mean PT or APTT and further studies are needed to bring about understanding of diabetic control and blood homeostasis.
Replicability and reproducibility in evolutionary optimization

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Fuzzing SMT solvers via two-dimensional input space exploration

Satisfiability Modulo Theories (SMT) solvers serve as the core engine of many techniques, such as symbolic execution. Therefore, ensuring the robustness and correctness of SMT solvers is critical. While fuzzing is an efficient and effective method for validating the quality of SMT solvers, we observe that prior fuzzing work only focused on generating various first-order formulas as the inputs but neglected the algorithmic configuration space of an SMT solver, which leads to under-reporting many deeply-hidden bugs. In this paper, we present Falcon, a fuzzing technique that explores both the formula space and the configuration space. Combining the two spaces significantly enlarges the search space and makes it challenging to detect bugs efficiently. We solve this problem by utilizing the correlations between the two spaces to reduce the search space, and introducing an adaptive mutation strategy to boost the search efficiency. During six months of extensive testing, Falcon finds 518 confirmed bugs in CVC4 and Z3, two state-of-the-art SMT solvers, 469 of which have already been fixed. Compared to two state-of-the-art fuzzers, Falcon detects 38 and 44 more bugs and improves the coverage by a large margin in 24 hours of testing.
Analysis of Digital Customer Communities in terms of their interactions during the first wave of the COVID-19 pandemic

Abstract The COVID-19 pandemic has redefined procedures in every business sector. In the first half of 2020, companies had to face an unexpected and unpredictable situation that resulted in both threats and opportunities in real time. Almost overnight, brick-and-mortar establishments of most stores closed, and the dominant part of transactions and activities moved from offline to the online environment. The main goal of the paper is to identify changes in the interactions of digital customer communities of selected e-business representatives in the Czech market. Within a sample of almost one and a half million Facebook users, during the first spring COVID-19 lockdown (March to May 2020), the interactions of the five largest Czech e-shops and their customers were recorded on a daily basis. The data were then subjected to a thorough statistical analysis in order to identify the specifics that resulted from the highly non-standard market situation. The results suggest that during the pandemic, there was a major reallocation of interactions in terms of their timing. Most interactions take place during the working week. From a day-to-day perspective, most of the interactions occur at around noon. In view of the findings, it can be stated that in order to maintain the competitiveness resulting from efficient management of corporate resources, it will be necessary to modify the usual procedures of e-marketing communication, as well as human resources management procedures in terms of the optimization of work of employees working from home.
Runtime detection of memory errors with smart status

C is a dominant language for implementing system software. Unfortunately, its support for low-level control of memory often leads to memory errors. Dynamic analysis tools, which have been widely used for detecting memory errors at runtime, are not yet satisfactory as they cannot deterministically and completely detect some types of memory errors, e.g., segment confusion errors, sub-object overflows, use-after-frees, and memory leaks. We propose Smatus, short for smart status, a new dynamic analysis approach that supports comprehensive runtime detection of memory errors. The key innovation is to create and maintain a small status node for each memory object. Our approach tracks not only the bounds of each pointer’s referent but also the status and reference count of the referent in its status node, where the status represents the liveness and segment type of the referent. A status node is smart as it is automatically destroyed when it becomes useless. To the best of our knowledge, Smatus represents the most comprehensive approach of its kind. In terms of effectiveness (for detecting more kinds of errors), Smatus outperforms state-of-the-art tools, Google’s AddressSanitizer, SoftBoundCETS and Valgrind. In terms of performance, Smatus outperforms SoftBoundCETS and Valgrind in terms of both time and memory overheads incurred, and is on par with AddressSanitizer in terms of the time and memory overheads tradeoff (with much lower memory overhead incurred).
Modular call graph construction for security scanning of Node.js applications

Most of the code in typical Node.js applications comes from third-party libraries that consist of a large number of interdependent modules. Because of the dynamic features of JavaScript, it is difficult to obtain detailed information about the module dependencies, which is vital for reasoning about the potential consequences of security vulnerabilities in libraries, and for many other software development tasks. The underlying challenge is how to construct precise call graphs that capture the connectivity between functions in the modules. In this work we present a novel approach to call graph construction for Node.js applications that is modular, taking into account the modular structure of Node.js applications, and sufficiently accurate and efficient to be practically useful. We demonstrate experimentally that the constructed call graphs are useful for security scanning, reducing the number of false positives by 81% compared to npm audit and with zero false negatives. Compared to js-callgraph, the call graph construction is significantly more accurate and efficient. The experiments also show that the analysis time is reduced substantially when reusing modular call graphs.
Empirical evaluation of smart contract testing: what is the best choice?

Security of smart contracts has attracted increasing attention in recent years. Many researchers have devoted themselves to devising testing tools for vulnerability detection. Each published tool has demonstrated its effectiveness through a series of evaluations on their own experimental scenarios. However, the inconsistency of evaluation settings such as different data sets or performance metrics, may result in biased conclusion. In this paper, based on an empirical evaluation of widely used smart contract testing tools, we propose a unified standard to eliminate the bias in the assessment process. First, we collect 46,186 source-available smart contracts from four influential organizations. This comprehensive dataset is open to the public and involves different code characteristics, vulnerability patterns and application scenarios. Then we propose a 4-step evaluation process and summarize the difference among relevant work in these steps. We use nine representative tools to carry out extensive experiments. The results demonstrate that different choices of experimental settings could significantly affect tool performance and lead to misleading or even opposite conclusions. Finally, we generalize some problems of existing testing tools, and propose some possible directions for further improvement.
Golgi pH and glycosylation

Glycans, as a part of glycoproteins, glycolipids and other glycoconjugates, are involved in many vital intraand inter-cellular tasks, such as protein folding and sorting, protein quality control, vesicular trafficking, cell signalling, immunological defence, cell motility and adhesion. Therefore, their correct construction is crucial for the normal functioning of eukaryotic cells and organisms they form. Most cellular glycans are constructed in the Golgi, and abnormalities in their structure may derive, for instance, from alkalinization of the Golgi lumen. In this work we show that Golgi pH is generally higher and more variable in abnormally glycosylating, i.e. strongly T-antigen (Gal-β1,3-GalNAc-ser/thr) expressing cancer cells, than in non-T-antigen expressing cells. We also confirmed that the Golgi pH alterations detected in cancer cells have the potential to induce glycosylation changes. A mere 0.2 pH unit increase in Golgi pH is able to induce T-antigen expression and inhibit terminal N-glycosylation in normally glycosylating cells. The mechanism of inhibition involves mislocalization of the corresponding glycosyltransferases. We also studied potential factors that can promote Golgi pH misregulation in health and disease, and found that cultured cancer cells, despite variation and elevation in Golgi pH, are fully capable of acidifying the Golgi lumen under the normal Golgi pH. Moreover, we introduce a Golgi localized Cl-/HCO3exchanger, AE2a, that participates in Golgi pH regulation by altering luminal bicarbonate concentration and thus also buffering capacity. Participation of AE2a in Golgi pH regulation is especially intriguing, because it also provides a novel mechanism for expelling protons from the Golgi lumen.
RAProducer: efficiently diagnose and reproduce data race bugs for binaries via trace analysis

A growing number of bugs have been reported by vulnerability discovery solutions. Among them, some bugs are hard to diagnose or reproduce, including data race bugs caused by thread interleavings. Few solutions are able to well address this issue, due to the huge space of interleavings to explore. What’s worse, in security analysis scenarios, analysts usually have no access to the source code of target programs and have troubles in comprehending them. In this paper, we propose a general solution RAProducer to efficiently diagnose and reproduce data race bugs, for both user-land binary programs and kernels without source code. The efficiency of RAProducer is achieved by analyzing the execution trace of the given PoC (proof-of-concept) sample to recognize race- and bug-related elements (including locks and shared variables), which greatly facilitate narrowing down the huge search space of data race spots and thread interleavings. We have implemented a prototype of RAProducer and evaluated it on 7 kernel and 10 user-land data race bugs. Evaluation results showed that, RAProducer is effective at reproducing all these bugs. More importantly, it enables us to diagnose 2 extra real world bugs which are left unconfirmed for a long time. It is also efficient as it reduces candidate data race spots of each bug to a small set, and narrows down the thread interleaving greatly.RAProducer is also more effective in reproducing real-world data race bugs than other state-of-the-art solutions.
Improving assertion oracles with evolutionary computation

Assertion oracles are executable boolean expressions placed inside a software program that verify the correctness of test executions. A perfect assertion oracle passes (returns true) for all correct executions and fails (returns false) for all incorrect executions. Because designing perfect assertion oracles is difficult, assertions often fail to distinguish between correct and incorrect executions. In other words, they are prone to false positives and false negatives. GAssert is the first technique to automatically improve assertion oracles by reducing false positives and false negatives. Given an assertion oracle and a set of correct and incorrect program states, GAssert employs a novel co-evolutionary algorithm that explores the space of possible assertions to identify one with fewer false positives and false negatives. Our evaluation on 34 Java methods shows that GAssert effectively improves assertion oracles.
Outer Membrane Vesicles Derived from Klebsiella pneumoniae Are a Driving Force for Horizontal Gene Transfer

Gram-negative bacteria release outer membrane vesicles (OMVs) into the extracellular environment. Recent studies recognized these vesicles as vectors to horizontal gene transfer, however the parameters that mediate OMVs transfer within bacterial communities remain unclear. The present study highlights for the first time the transfer of plasmids containing resistance genes via OMVs derived from Klebsiella pneumoniae (K. pneumoniae). This mechanism confers DNA protection and it is plasmid copy number dependent with a ratio of 3.6 time among high copy-number plasmid (pGR) versus low copy number plasmid (PRM) and the transformation efficiency was 3.6 times greater. Therefore, the DNA amount in the vesicular lumen and the efficacy of horizontal gene transfer was strictly dependent on the identity of the plasmid. Moreover, the role of K. pneumoniae-OMVs in interspecies transfer was described. The transfer ability was not related to the phylogenetic characteristics between the donor and the recipient species. K. pneumoniae-OMVs transferred plasmid to Escherichia coli, Salmonella enterica, Pseudomonas aeruginosa and Burkholderia cepacia. These findings address the pivotal role of K. pneumoniae-OMVs as vectors for antimicrobial resistance genes spread, contributing to the development of antibiotic resistance in the microbial communities. Author summary K. pneumoniae is an important opportunistic pathogen that affects several host districts, in particular respiratory and urinary tracts. Hospital-acquired K. pneumoniae infections lead to a 50% mortality rate correlated with rapid acquisition of antibiotic resistance. Currently, the increasing rate of antibiotic resistance among K. pneumoniae isolates is a major concern worldwide. The spread of multidrug-resistant K. pneumoniae strains renders current therapeutic options ineffective. Like all Gram-negative bacteria, K. pneumoniae secretes OMVs. OMVs are spherical structures, with a diameter between 50-250 nm, originating from the outer membrane. OMVs biogenesis allows bacteria to interact with the external environment, increasing bacterial survival under stressful conditions and regulating microbial interactions within bacterial communities. Few evidence recognized OMVs as vectors for horizontal gene transfer, contributing to the spread of resistance. In this scenario, the present study examines the potential role of K. pneumoniae-OMVs in inter- and intra-species diffusion of ß-lactam resistance.
Relaxation time asymmetry in stator dynamics of the bacterial flagellar motor

The bacterial flagellar motor (BFM) is the membrane-embedded rotary molecular motor which turns the flagellum that provides thrust to many bacterial species. This large multimeric complex, composed of a few dozen constituent proteins, has emerged as a hallmark of dynamic subunit exchange. The stator units are inner-membrane ion channels which dynamically bind and unbind to the peptidoglycan at the rotor periphery, consuming the ion motive force (IMF) and applying torque to the rotor when bound. The dynamic exchange is known to be a function of the viscous load on the flagellum, allowing the bacterium to dynamically adapt to its local viscous environment, but the molecular mechanisms of exchange and mechanosensitivity remain to be revealed. Here, by actively perturbing the steady-state stator stoichiometry of individual motors, we reveal a stoichiometry-dependent asymmetry in stator remodeling kinetics. We interrogate the potential effect of next-neighbor interactions and local stator unit depletion and find that neither can explain the observed asymmetry. We then simulate and fit two mechanistically diverse models which recapitulate the asymmetry, finding stator assembly dynamics to be particularly well described by a two-state catch-bond mechanism.
Brain-like functional specialization emerges spontaneously in deep neural networks

The last quarter century of cognitive neuroscience has revealed numerous cortical regions in humans with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what other people are thinking. But it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception, using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects the computational requirements of the task. We find that networks trained on generic object recognition perform poorly on face recognition and vice versa, and further that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. Thus, generic visual features that suffice for object recognition are apparently suboptimal for face recognition and vice versa. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.
A polygenic architecture with conditionally neutral effects underlies ecological differentiation in Silene

Ecological differentiation can drive speciation but it is unclear how the genetic architecture of habitat-dependent fitness contributes to lineage divergence. We investigated the genetic architecture of cumulative flowering, a fitness component, in second-generation hybrids between Silene dioica and S. latifolia transplanted into the natural habitat of each species. We used reduced-representation sequencing and Bayesian Sparse Linear Mixed Models (BSLMMs) to analyze the genetic control of cumulative flowering in each habitat. Our results point to a polygenic architecture of cumulative flowering. Allelic effects were mostly beneficial or deleterious in one habitat and neutral in the other. The direction of allelic effects was associated with allele frequency differences between the species: positive-effect alleles were often derived from the native species, whereas negative-effect alleles, at other loci, tended to originate from the non-native species. We conclude that ecological differentiation is governed and maintained by many loci with small, conditionally neutral effects. Conditional neutrality may result from differences in selection targets in the two habitats and provides hidden variation upon which selection can act. Polygenic architectures of adaptive differentiation are expected to be transient during lineage divergence and may therefore be unrelated to high genetic differentiation at the underlying loci.