Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring and Characterization of Miscellaneous Electrical Loads in a Large Retail Environment

Buildings account for 40% of primary energy consumption in the United States (residential 22%; commercial 18%). Most (70% residential and 79% commercial) is used as electricity. Thus, almost 30% of U.S. primary energy is used to provide electricity to buildings. Plug loads play an increasingly critical role in reducing energy use in new buildings (because of their increased efficiency requirements), and in existing buildings (as a significant energy savings opportunity). If all installed commercial building miscellaneous electrical loads (CMELs) were replaced with energy-efficient equipment, a potential annual energy saving of 175 TWh, or 35% of the 504 TWh annual energy use devoted to MELs, could be achieved. This energy saving is equivalent to the annual energy production of 14 average-sized nuclear power plants. To meet DOE's long-term goals of reducing commercial building energy use and carbon emissions, the energy efficiency community must better understand the components and drivers of CMEL energy use, and develop effective reduction strategies. These goals can be facilitated through improved data collection and monitoring methodologies, and evaluation of CMELs energy-saving techniques.
Memory Space Recycling

Many program codes from different application domains process very large amounts of data, making their cache memory behavior critical for high performance. Most of the existing work targeting cache memory hierarchies focus on improving data access patterns, e.g., maximizing sequential accesses to program data structures via code and/or data layout restructuring strategies. Prior work has addressed this data locality optimization problem in the context of both single-core and multi-core systems. Another dimension of optimization, which can be as equally important/beneficial as improving data access pattern is to reduce the data volume (total number of addresses) accessed by the program code. Compared to data access pattern restructuring, this volume minimization problem has relatively taken much less attention. In this work, we focus on this volume minimization problem and address it in both single-core and multi-core execution scenarios. Specifically, we explore the idea of rewriting an application program code to reduce its "memory space footprint". The main idea behind this approach is to reuse/recycle, for a given data element, a memory location that has originally been assigned to another data element, provided that the lifetimes of these two data elements do not overlap with each other. A unique aspect is that it is "distance aware", i.e., in identifying the memory/cache locations to recycle it takes into account the physical distance between the location of the core and the memory/cache location to be recycled. We present a detailed experimental evaluation of our proposed memory space recycling strategy, using five different metrics: memory space consumption, network footprint, data access distance, cache miss rate, and execution time. The experimental results show that our proposed approach brings, respectively, 33.2%, 48.6%, 46.5%, 31.8%, and 27.9% average improvements in these metrics, in the case of single-threaded applications. With the multi-threaded versions of the same applications, the achieved improvements are 39.5%, 55.5%, 53.4%, 26.2%, and 22.2%, in the same order.
Open-channel flow over evolving subaqueous ripples

Abstract We have numerically investigated the turbulent flow and sediment grain motion in an open-channel flow configuration over a subaqueous sediment bed featuring two-dimensional transverse ripples at moderate Reynolds number and super-critical Shields number values. The simulation data, which were generated by means of particle-resolved direct numerical simulation, are the same as in our previous work (Kidanemariam & Uhlmann, J. Fluid Mech., vol. 818, 2017, pp. 716–743). By carefully choosing the computational box sizes, we were able to accommodate single ripple units which form over an initially flat sediment bed at a wavelength equal to the domain length. The ripples then evolve into their asymmetric shape relatively quickly and eventually migrate downstream steadily while maintaining their shape and size. In the present study, using a ripple-conditioned phase-averaging procedure, we are able to obtain novel insights into the evolution of the turbulent flow and particle motion over the bedforms, in particular the spatial structure of the basal shear stress and its relation to the particle flow rate. Our analysis confirms that the boundary shear-stress maximum is located upstream of the ripple crest, while the particle flow rate is essentially in phase with the ripple topology, with an average phase difference between the two in the range of 18–19 particle diameters for the considered parameter values. We were further able to confirm the link between the sediment flux relaxation behaviour and the observed shear-stress/geometry lag, by direct evaluation of the saturation length scale.
Anomalies in global network connectivity associated with early recovery from alcohol dependence: A network transcranial magnetic stimulation and electroencephalography study

Although previous research in alcohol dependent populations identified alterations within local structures of the addiction ‘reward’ circuitry, there is limited research into global features of this network, especially in early recovery. Transcranial magnetic stimulation (TMS) is capable of non‐invasively perturbing the brain network while electroencephalography (EEG) measures the network response. The current study is the first to apply a TMS inhibitory paradigm while utilising network science (graph theory) to quantify network anomalies associated with alcohol dependence. Eleven individuals with alcohol‐dependence (ALD) in early recovery and 16 healthy controls (HC) were administered 75 single pulses and 75 paired‐pulses (inhibitory paradigm) to both the left and right prefrontal cortex (PFC). For each participant, Pearson cross‐correlation was applied to the EEG data and correlation matrices constructed. Global network measures (mean degree, clustering coefficient, local efficiency and global efficiency) were extracted for comparison between groups. Following administration of the inhibitory paired‐pulse TMS to the left PFC, the ALD group exhibited altered mean degree, clustering coefficient, local efficiency and global efficiency compared to HC. Decreases in local efficiency increased the prediction of being in the ALD group, while all network metrics (following paired‐pulse left TMS) were able to adequately discriminate between the groups. In the ALD group, reduced mean degree and global clustering was associated with increased severity of past alcohol use. Our study provides preliminary evidence of altered network topology in patients with alcohol dependence in early recovery. Network anomalies were predictive of high alcohol use and correlated with clinical features of alcohol dependence. Further research using this novel brain mapping technique may identify useful network biomarkers of alcohol dependence and recovery.
The evolution of recombination in self-fertilizing organisms

Cytological data from flowering plants suggest that the evolution of recombination rates is affected by the mating system of organisms, as higher chiasma frequencies are often observed in self-fertilizing species compared with their outcrossing relatives. Understanding the evolutionary cause of this effect is of particular interest, as it may shed light on the selective forces favoring recombination in natural populations. While previous models showed that inbreeding may have important effects on selection for recombination, existing analytical treatments are restricted to the case of loosely linked loci and weak selfing rates, and ignore the effect of genetic interference (Hill-Robertson effect), known to be an important component of selection for recombination in randomly mating populations. In this article, we derive general expressions quantifying the stochastic and deterministic components of selection acting on a mutation affecting the genetic map length of a whole chromosome along which deleterious mutations occur, valid for arbitrary selfing rates. The results show that selfing generally increases selection for recombination caused by interference among mutations as long as selection against deleterious alleles is sufficiently weak. While interference is often the main driver of selection for recombination under tight linkage or high selfing rates, deterministic effects can play a stronger role under intermediate selfing rates and high recombination, selecting against recombination in the absence of epistasis, but favoring recombination when epistasis is negative. Individual-based simulation results indicate that our analytical model often provides accurate predictions for the strength of selection on recombination under partial selfing. AUTHOR SUMMARY Understanding the evolutionary advantage of sex and the associated process of meiotic recombination remains a major challenge in evolutionary biology. Useful insights on this question may possibly be gained from exploring the causes of genetic variation for recombination rates within and between species. In particular, a remarkable pattern observed in flowering plants is that self-fertilizing species tend to have higher rates of meiotic crossovers than their outcrossing relatives. In this article, we use analytical and simulation methods to investigate how selfing affects the sign and magnitude of stochastic and deterministic components of selection for recombination in hermaphroditic organisms. The results show that, while selection for recombination vanishes under complete selfing (as recombination has no genetic effect in fully homozygous individuals), it is often maximized for selfing rates slightly below one. This is primarily due to the fact that selfing increases the strength of selective interference among loci in finite populations, and may explain the higher crossover rates observed in inbred plant species.
Resource requirements for ecotoxicity testing: A comparison of traditional and new approach methods

Toxicity testing is under transformation as it aims to harness the potential of New Approach Methods (NAMs) as alternative test methods that may be less resource intensive (i.e., fewer animals, cheaper costs, quicker assays) than traditional approaches while also providing more data and information. While many stakeholders are of the opinion that this unfolding transformation holds significant promise as a more efficient and ethical way forward, few studies have compared the resources required for NAMs versus those needed for traditional animal-based toxicity tests, particularly in the field of ecotoxicology. The objective was to compare resources needed for traditional animal-based ecotoxicity tests versus alternative tests using emergent NAMs. From a bibliometric review, we estimate that traditional tests for a single chemical cost $118,000 USD, require 135 animals, and take 8 weeks. In comparison, alternative tests cost $2,600, require 20 animals (or none), and take up to 4 weeks to test 16 (to potentially hundreds of) chemicals. Based on our analysis we conclude that NAMs in ecotoxicology can be more advantageous than traditional methods in terms of resources required (i.e., monetary costs, number of animals needed, and testing times). We note, however, that the evidence underpinning these conclusions is relatively sparse. Moving forward, groups developing and applying NAMs should provide more detailed accounts of the resources required. In addition, there is also a need for carefully designed case studies that demonstrate the domain of applicability of NAMs (and make comparisons to traditional tests) to ultimately build confidence among the user community.
Single-deletion-mutant, third-generation rabies viral vectors allow nontoxic retrograde targeting of projection neurons with greatly increased efficiency

Rabies viral vectors have become important components of the systems neuroscience toolkit, allowing both direct retrograde targeting of projection neurons and monosynaptic tracing of inputs to defined postsynaptic populations, but the rapid cytotoxicity of first-generation (ΔG) vectors limits their use to short-term experiments. We recently introduced second-generation, double-deletion-mutant (ΔGL) rabies viral vectors, showing that they efficiently retrogradely infect projection neurons and express recombinases effectively but with little to no detectable toxicity; more recently, we have shown that ΔGL viruses can be used for monosynaptic tracing with far lower cytotoxicity than the first-generation system. Here we introduce third-generation (ΔL) rabies viral vectors, which, like first-generation vectors, have only a single gene deleted from their genomes (in this case the viral polymerase gene L) but which appear to be as nontoxic as second-generation ones, based on in vivo longitudinal structural and functional two-photon imaging and ex vivo electrophysiology. Although third-generation vectors are therefore phenotypically very similar to second-generation ones, we show that they have the major advantage of growing to much higher titers, and this key difference results in significantly increased numbers of retrogradely labeled neurons in vivo. These ΔL rabies viral vectors therefore constitute a new state of the art for minimally perturbative, pathway-specific expression of recombinases and transactivators in mammalian neurons selected on the basis of their axonal projections. Because replication of deletion-mutant rabies viruses within complementing cells is precisely the process that underlies monosynaptic tracing, the higher replication efficiency of this new class of rabies viral vectors furthermore suggests the potential to provide the foundation of an improved nontoxic monosynaptic tracing system.
Copepod functional traits and groups show divergent biogeographies in the global ocean

Aim The distribution of zooplankton functional traits is a key factor for regulating food web dynamics and carbon cycling in the oceans. Yet, we lack a clear understanding of how many functional groups (FGs) exist in the zooplankton and how their traits are distributed on a global scale. Here, we model and map the environmental habitats of copepod (i.e. the main component of marine zooplankton) FGs to identify regions sharing similar functional trait expression, at the community level. Taxon Marine planktonic Neocopepoda. Location Global ocean. Methods Factor analysis on mixed data and hierarchical clustering were used to identify copepod FGs based on five species-level functional traits. An ensemble of species distribution models was used to estimate the environmental niches of the species modelled and the community weighted mean values of the traits studied. Ocean regions were defined based on their community-level mean trait expression using a principal component analysis and hierarchical clustering. Results Eleven global copepod FGs were identified. They displayed contrasting latitudinal patterns in mean annual habitat suitability that could be explained by differences in environmental niche preferences: two FGs were associated with polar conditions, one followed the global temperature gradient, five were associated with tropical oligotrophic gyres, and the remaining three with boundary currents and counter currents. Four main regions of varying community weighted mean trait values emerged: the Southern Ocean, the northern and southern high latitudes, the tropical gyres, and the boundary currents and upwelling systems. Conclusions The present FGs will improve the representation of copepods in global marine ecosystem models. This study improves the understanding of the patterns and drivers of copepods trait biogeography and will serve as a basis for studying links between zooplankton biodiversity and ecosystem functioning in a context of climate change.
High Cellular Uptake Gene Delivery Platform with Chitosan and L-arginine Complex for Cancer Treatment

Gene therapy outperforms chemotherapy in limiting the harm to human body. A major impediment to the progress of gene therapy is a lack of proper carriers. Chitosan, derived from shellfish, is a desirable candidate for nucleic acid delivery because of its outstanding biodegradability and biocompatibility. However, previous works show that chitosan has poor cell membrane permeability. In this study, we synthesize a complex delivery system for gene therapy by compounding chitosan with L-arginine, and test the encapsulation, uptake, and cancer cell treatment power. Encapsulation efficiency of the chitosan/L-arginine complex (CA) can reach more than 90% in weight. Flow cytometry reveal that siRNA delivered by the complex can be taken in by cells 10 times better than free siRNA and 2 times better than chitosan alone. Confocal imaging confirmed the high cellular uptake, and siRNA tests in HeLa cells indicate the successful silencing of the target gene (RRM2). The complex serves as a gene delivery system that is capable of loading and delivering a variety of genetic materials in which the specific nucleic acid molecule to be delivered could be altered at will.
Research about the Quality of Bulk Titanium Materials Obtained by Powder Metallurgy Technology from Titanium Hydride Powder Used for Automotive Components

Titanium has excellent mechanical properties, low density, high chemical stability, good heat transfer properties and a good corrosion resistance. In recent years, a new process technology is emerging by which titanium and titanium alloys can be made by sintering titanium hydride (TiH2) and its mixture with alloying elements. The feasibility of this manufacturing approach has been fully demonstrated from powder to sintering and from microstructure to mechanical properties. This paper describes a study concerning Powder Metallurgy (PM) technology processing of Ti by conventional PM and non-conventional PM method such as Spark Plasma Sintering (SPS) route. The influence of the technological parameters on the micro-hardness and SEM microstructures during bulk titanium materials processing has been studied. The STATISTICA program has been used to monitor the influence of the technological parameters on the micro-hardness of bulk titanium processed products. The tribological behaviour of the bulk titanium materials on the basis of the coefficient of friction and wear rate is presented, too.
The gene signatures of human alpha cells in types 1 and 2 diabetes indicate disease-specific pathways of alpha cell dysfunction

Glucagon secretion is perturbed in both type 1 and type 2 diabetes (T1D, T2D) the pathophysiological changes at the level of individual pancreatic alpha cells are still largely obscure. Using recently-curated single-cell RNA data from human donors with either T1D or T2D and appropriate controls, we leveraged alpha cell transcriptomic alterations consistent with both common and discrete pathways. Firstly, altered expression of genes associated with alpha cell identity (ARX, MAFB) was common to both diseases. In contrast, increased expression of cytokine-regulated genes and genes involved in glucagon biosynthesis and processing were apparent in T1D, whereas mitochondrial genes associated with reactive oxygen species generation (COX7B, NQO2) were dysregulated in alpha cells from T2D patients. Conversely, T1D alpha cells displayed alterations in genes associated with autoimmune-induced ER stress (ERLEC1, HSP90) whilst those from T2D patients showed changes in glycolytic and citrate cycle genes (LDH, PDHB, PDK4) which were unaffected in T1D. These findings suggest that despite some similarities related to loss-of-function, the alterations of alpha cells present important disease-specific signatures, suggesting that they are secondary to the main pathogenic events characteristic to each disease, namely immune-mediated-or metabolic-mediated-stress in respectively T1D and T2D.
Asynchronous Rumor Spreading in Dynamic Graphs

We study asynchronous rumor spreading algorithm in dynamic and static graphs. In the asynchronous rumor spreading, for a given underlying graph, each node is equipped with an exponential time clock of rate 1. When a node’s clock ticks, the node calls a random neighbor in order to exchange a rumor, if at least one of them knows it. Assuming a single node knows a rumor, we apply a differential equation-based technique to obtain an upper bound for the spread time of the algorithm in general dynamic graphs, which is the first time when all nodes get informed with high probability. In particular, we derive an upper bound for the spread time of the algorithm in a discrete version of a geometric mobile network, introduced by Clementi et al. [7]. In this model, a set of n agents independently performs random walks on a √ n × √ n plane and every two agents are able to communicate if they are within Euclidean distance at most R , where f ( n ) √ log n ⩽ R ⩽ √ n and f ( n ) is a slowly growing function in n . Here, we show that the algorithm spreads a rumor through the network in O (log n + √ n/R ) time, with high probability. Although we only show an upper bound the spread time of the algorithm in a 2 dimensional space, the framework can be also applied for geometric mobile networks defined over higher dimensional space and other random dynamic evolving networks such as stationary edge-Markovian model. Besides these synchronous and discrete dynamic models, we also consider the spreading time in dynamical Erdős-Rényi graphs.
Recoverable and Detectable Fetch&Add

The emergence of systems with non-volatile main memory (NVRAM) increases the need for persistent concurrent objects. Of specific interest are recoverable implementations that, in addition to being robust to crash-failures, are also detectable . Detectability ensures that upon recovery, it is possible to infer whether the failed operation took effect or not and, in the former case, obtain its response. This work presents two recoverable detectable Fetch&Add ( FAA ) algorithms that are self-implementations , i.e, use only a fetch&add base object, in addition to read/write registers. The algorithms target two different models for recovery: the global-crash model and the individual-crash model. In both algorithms, operations are wait-free when there are no crashes, but the recovery code may block if there are repeated failures. We also prove that in the individual-crash model, there is no implementation of recoverable and detectable FAA using only read, write and fetch&add primitives in which all operations, including recovery, are lock-free.