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Construction of municipal solid waste (MSW) in open dump sites is a popular method in developing countries, for financial reasons. Although the side slopes of these non-engineered dump sites are stable under static conditions, they can easily fail, if excessive water infiltration or sever earthquakes occur. The purpose of this study is to evaluate the probabilistic stability of a failed dump site in Iran considering the uncertainties in shear strength parameters and unit weight of the waste material as well as the variability in seismic forces. Under and post-construction probabilistic slope stability analyses were conducted under static and seismic loads. The results of the analysis of the under construction failed cell shows that the slope is stable under static and seismic loads and the probability of instability is zero. In the case of excessive infiltration from water flow, it is shown that the slope has high risk of failure. The post-construction probability analysis reveals that, for slope angles ranging from 40 to 45° and slope heights ranging from 35 to 50 m the slope is stable under both static and seismic loads. Based on the results of this study, several recommendations are presented for the construction of open dump sites in Iran.

Graphene-based materials have shown immense pertinence for sensing/imaging, gene/drug delivery, cancer therapy/diagnosis, and tissue engineering/regenerative medicine. Indeed, the large surface area, ease of functionalization, high drug loading capacity, and reactive oxygen species induction potentials have rendered graphene- (G-) and graphene oxide (GO)-based (nano)structures promising candidates for cancer therapy applications. Various techniques namely liquid-phase exfoliation, Hummer's method, chemical vapor deposition, chemically reduced GO, mechanical cleavage of graphite, arc discharge of graphite, and thermal fusion have been deployed for the production of G-based materials. Additionally, important criteria such as biocompatibility, bio-toxicity, dispersibility, immunological compatibility, and inflammatory reactions of G-based structures need to be systematically assessed for additional clinical and biomedical appliances. Furthermore, surface properties (e.g., lateral dimension, charge, corona influence, surface structure, and oxygen content), concentration, detection strategies, and cell types are vital for anticancer activities of these structures. Notably, the efficient accumulation of anticancer drugs in tumor targets/tissues, controlled cellular uptake properties, tumor-targeted drug release behavior, and selective toxicity toward the cells are crucial criteria that need to be met for developing future anticancer G-based nanosystems. Herein, important challenges and future perspectives of cancer therapy using G- and GO-based nanosystems have been highlighted, and the recent advancements are deliberated.

Black titanium dioxide is a broadband-absorbing photocatalyst characterized by integration of atomic hydrogen within the outer layers of anatase nanomaterials. In addition to is broad absorbance of visible to near-infrared light, the material has some intrinsic upconversion ability, which is greatly enhanced by the addition of a niobium oxide shell. This yields a highly active photocatalyst as demonstrated in photodegradation experiments.

International audience; Different heterogeneous simulation components can be integrated to produce a more effective complex global system. The IEEE High-Level Architecture (HLA) is an international standard that promotes interoperability and reusability for distributed simulation (DS). This paper proposes a DS system that integrates an agent-based and discrete-event simulator with a 3D game engine to build virtual reality (VR) applications that replicate real environments. In this case study, AnyLogic is used as an agent-based and discrete event simulator to simulate the process flow and COVID-19 transmission inside the University Health Network dialysis unit, Toronto, Canada. Unity game engine delivers the 3D modelling replicating the real architecture and environment of the dialysis unit. The HLA standard plays a major role in the integration of AnyLogic and Unity to produce a more effective and powerful DS system for VR applications.

A simple, fast and reliable graphene oxide nanosheets based dispersive solid-phase microextraction methodology was described for the quantification of trace amount of ethambutol. The determination of ethambutol quantified with the aim of ion mobility spectroscopy as a sensitive, rapid, inexpensive and environmentally friendly instrument. Effects of relevant experimental parameters on the method efficiency such as pH, type of buffer and its volume, amount of absorbent, desorption solvent and extract time were investigated to reach the maximum efficiency of the proposed method. Under the optimum conditions, the calibration curve was linear in the range of 1 to 120 μg L−1 with the R-squared (R2) of 0.9990. The limit of detection for proposed method (n = 8) was 0.4 μg L−1 and the relative standard deviations were obtained (n = 8) 3.3% and 1.6% for 10 and 100 μg L−1, respectively. The proposed method was successfully applied for the preconcentration and determination of ethambutol in the different biological samples such as plasma, saliva, breast milk and artificial tear.

Background: Preeclampsia is a global burden with 10 million incidences annually and 210 daily deaths worldwide. Diagnosis is mainly based on the features following full presentation. Objective: This study explored whether early pregnancy circadian changes of ambulatory blood pressure monitoring (ABPM) could predict preeclampsia and hypertension. Methods: In a prospective study, 294 pregnant women who were referred to Sarem Women’s Hospital, Iran were recruited. Systolic, diastolic and mean arterial pressures (MAP) were recorded (diurnally and nocturnally) in each trimester. Dipping was defined as a minimum 10% decrease in blood pressure. Results: Of the 251 women who completed the study, 25 percent (n=63) experienced blunted MAP dipping during sleep phases in the second trimester. Eighty-nine percent (n=56) experienced hypertensive disorder in the third trimester, one-third of which experienced preeclampsia. Of the women with normal MAP dipping (n=188), 5 percent (n=10) had gestational hypertension and 1 percent (n=2) became preeclamptic. (P Conclusion: This study clearly demonstrated blunted blood pressure dipping overnight during the second trimester which is a strong predictor of forthcoming pregnancy-induced hypertension and preeclampsia. A scoring system was developed to predict hypertensive disorder and it was significantly correlated with preeclampsia occurrence.

Of late, deep neural networks have become ubiquitous in mobile applications. As mobile devices generally require immediate response while maintaining user privacy, the demand for on-device machine learning technology is on the increase. Nevertheless, mobile devices suffer from restricted hardware resources, whereas deep neural networks involve considerable computation and communication. Therefore, the implementation of a neural-network specialized hardware accelerator, generally called neural processing unit (NPU), has started to gain attention for the mobile application processor (AP). However, NPUs for commercial mobile AP face two challenges that are difficult to realize simultaneously: execution of a wide range of applications and efficient performance. In this paper, we propose a flexible but efficient NPU architecture for a Samsung flagship mobile system-on-chip (SoC). To implement an efficient NPU, we design an energy-efficient inner-product engine that utilizes the input feature map sparsity. We propose a re-configurable MAC array to enhance the flexibility of the proposed NPU, dynamic internal memory port assignment to maximize on-chip memory bandwidth utilization, and efficient architecture to support mixed-precision arithmetic. We implement the proposed NPU using the Samsung 5nm library. Our silicon measurement experiments demonstrate that the proposed NPU achieves 290.7 FPS and 13.6 TOPS/W, when executing an 8-bit quantized Inception-v3 model [1] with a single NPU core. In addition, we analyze the proposed zero-skipping architecture in detail. Finally, we present the findings and lessons learned when implementing the commercial mobile NPU and interesting avenues for future work.

As the number of weight parameters in deep neural networks (DNNs) continues growing, the demand for ultra-efficient DNN accelerators has motivated research on non-traditional architectures with emerging technologies. Resistive Random-Access Memory (ReRAM) crossbar has been utilized to perform insitu matrix-vector multiplication of DNNs. DNN weight pruning techniques have also been applied to ReRAM-based mixed-signal DNN accelerators, focusing on reducing weight storage and accelerating computation. However, the existing works capture very few peripheral circuits features such as Analog to Digital converters (ADCs) during the neural network design. Unfortunately, ADCs have become the main part of power consumption and area cost of current mixed-signal accelerators, and the large overhead of these peripheral circuits is not solved efficiently. To address this problem, we propose a novel weight pruning framework for ReRAM-based mixed-signal DNN accelerators, named TINYADC, which effectively reduces the required bits for ADC resolution and hence the overall area and power consumption of the accelerator without introducing any computational inaccuracy. Compared to state-of-the-art pruning work on the ImageNet dataset, TINYADC achieves 3.5× and 2.9× power and area reduction, respectively. TINYADC framework optimizes the throughput of state-of-the-art architecture design by 29% and 40% in terms of the throughput per unit of millimeter square and watt (GOPs/s×mm2and GOPs/w), respectively.

The COVID-19 pandemic has been particularly threatening to the patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

This research presents a fast, sensitive, and selective ultrasound-assisted dispersive solid phase microextraction technique for simultaneous preconcentration and determination of ultra-trace amount of carbamazepine and phenobarbital. Reduced graphene oxide sheets decorated with cadmium sulfide quantum dots was synthesized, characterized, and used as a high capacity adsorbent. A high performance liquid chromatography with UV detector (HPLC-UV) instrument with 58:42 composition of methanol:acetic acid/acetate buffer (pH = 5; 0.05 mol L-1) as the mobile phase and set on the wavelength of 230 nm was used to separate and quantify the analytes. In this regard, different parameters affecting adsorption and desorption of the analytes on the surface of the nanocomposite were studied and optimized to maximize the efficiency of the method. The method was linear in the ranges of 0.5-180 and 0.5-140 ng mL−1 (correlation coefficient (r) > 0.999) with limits of detection of 0.19 and 0.24 ng mL−1 for carbamazepine and phenobarbital, respectively. Eventually, to evaluate the efficiency of the proposed method for the determination of pharmaceuticals in biological samples, different real samples including breast milk, urine and human plasma were tested. Obtained recoveries values were within the range of 96.3 ± 2 to 103.7 ± 3.3% which showed satisfactory efficiency.

This paper examines the design space trade-offs of DNNs accelerators aiming to achieve competitive performance and efficiency metrics for all four combinations of dense or sparse activation/weight tensors. To do so, we systematically examine the overheads of supporting sparsity on top of an optimized dense core. These overheads are modeled based on parameters that indicate how a multiplier can borrow a nonzero operation from the neighboring multipliers or future cycles. As a result of this exploration, we identify a few promising designs that perform better than prior work. Our findings suggest that even the best design targeting dual sparsity yields a 20%-30% drop in power efficiency when performing on single sparse models, i.e., those with only sparse weight or sparse activation tensors. We found that one can reuse resources of the same core to maintain high performance and efficiency when running single sparsity or dense models. We call this hybrid architecture Griffin. Griffin is 1.2, 3.0, 3.1, and 1.4X more power-efficient than state-of-the-art sparse architectures, for dense, weight-only sparse, activation-only sparse, and dual sparse models, respectively., Comment: Accepted at the 28th IEEE International Symposium on High-Performance Computer Architecture - HPCA 2022

Background: The coronavirus disease 2019 (COVID-19) presents various phenotypes from asymptomatic involvement to death. Disseminated intravascular coagulopathy (DIC) is among the poor prognostic complications frequently observed in critical illness. To improve mortality, a timely diagnosis of DIC is essential. The International Society on Thrombosis and Hemostasis (ISTH) introduced a scoring system to detect overt DIC (score ≥5) and another category called sepsis-induced coagulopathy (SIC) to identify the initial stages of DIC (score ≥4). This study aimed to determine whether clinicians used these scoring systems while assessing COVID-19 patients and the role of relevant biomarkers in disease severity and outcome. Materials and Methods: An exhaustive search was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, using Medline, Embase, Cochrane, CINAHL, and PubMed until August 2020. Studies considering disease severity or outcome with at least two relevant biomarkers were included. For all studies, the definite, maximum, and minimum ISTH/SIC scores were calculated. Results: A total of 37 papers and 12,463 cases were reviewed. Studies considering ISTH/SIC criteria to detect DIC suggested a higher rate of ISTH ≥5 and SIC ≥4 in severe cases and nonsurvivors compared with nonsevere cases and survivors. The calculated ISTH scores were dominantly higher in severe infections and nonsurvivors. Elevated D-dimer was the most consistent abnormality on admission. Conclusion: Higher ISTH and SIC scores positively correlate with disease severity and death. In addition, more patients with severe disease and nonsurvivors met the ISTH and SIC scores for DIC. Given the high prevalence of coagulopathy in COVID-19 infection, dynamic monitoring of relevant biomarkers in the form of ISTH and SIC scoring systems is of great importance to timely detect DIC in suspicious patients.

Coagulopathy and derangements in the coagulation parameters are significant features of COVID-19 infection, which increases the risk of disseminated intravascular coagulation, thrombosis, and hemorrhage in these patients, resulting in increased morbidity and mortality. In times of COVID-19, special consideration should be given to patients with concurrent chronic kidney disease (CKD) and COVID-19 (CKD/COVID-19 patients) as renal dysfunction increases their risk of thrombosis and hemorrhage, and falsely affects some of the coagulation factors, which are currently utilized to assess thrombosis risk in patients with COVID-19. Hence, we believe extra attention should be given to determining the risk of thrombosis and bleeding and optimizing the timing and dosage of anticoagulant therapy in this unique population of patients. CKD/COVID-19 patients are considered a high-risk population for thrombotic events and hemorrhage. Furthermore, effects of renal function on paraclinical and clinical data should be considered during the evaluation and interpretation of thrombosis risk stratification. Individualized evaluation of clinical status and kidney function is necessary to determine the best approach and management for anticoagulant therapy, whereas there is a lack of studies about the population of CKD/COVID-19 patients who need anticoagulant therapy now.

PURPOSE The commonly used 24-hour collection technique has been the mainstay of diagnosis for supersaturation but has some certain limitations. Hence, superiority of multiple short urine collections as a new alternative in precipitation risk assessment was assessed compared to the standard 24-hour urine collection among healthy subjects. MATERIALS AND METHODS Individual urine samples of 26 healthy subjects were acquired every 2 to 3 hours throughout the 24 hours. Urine samples were obtained and the time and volume of each sample were recorded. Urinary constituents involved in precipitation including, sodium-potassium, chloride, calcium, phosphate, citrate, magnesium, urea, creatinine and pH were measured. A simulated 24-hour collection was recalculated by the totalling of all shorter urine collections volume and urinary constituents excretions throughout the day. RESULTS Urine pH, urine creatinine and precipitation rate had a significantly lower values in 24-hours urine collection compared to one individual value of multiple urine collections by -0.769 (P

This paper aims to investigate the reduction of the internal erosion by inclusion of fiber in the context of the base sandy soil next to the filter. Two samples overlain by a gravel filter, and with different grain sizes, (i.e., a poorly graded sand, and a poorly graded sand with clay) were used in this study. The specimens with various relative densities were exposed to one-dimensional water flow under 1 bar pressure, and erosion of the base soils without reinforcement along with fiber-reinforced soils through the coarse grain filter was measured. Polypropylene fiber content was adjusted to 0.50, 0.75, and 1.0% by weight of the base soils. The mass of the soil clogged in the filter plus the ones suspended in the water were quantified to determine mass of internal erosion for each specimen. The results indicated that internal erosion and permeability decrease with increase in fiber reinforcement and relative density. In addition, reinforcing sands without or with fines could change their erosion category from some-erosion to no-erosion and from excessive-erosion to some-erosion respectively.

Over the past four decades, every generation of processors has delivered 2x performance boost, as predicted by Moore's law. Ironically, the end of Moore's law occurred at almost the same time as computationally intensive deep learning algorithms were emerging. Deep neural networks (DNNs) offer state-of-the-art solutions for many applications, including computer vision, speech recognition, and natural language processing. However, this is just the tip of the iceberg. Deep learning is taking over many classic machine -learning applications and also creating new markets, such as autonomous vehicles, which will tremendously amplify the demand for even more computational power.

Black titanium dioxide (bTiO2) is a new narrow bandgap photocatalyst that exploits the full solar spectrum with applications in sustainable environmental remediation and energy harvesting. Composition of bTiO2 with 5 wt% of reduced graphene oxide (RGO) increases the Rhodamine B photodegradation rate by 3.2 and 12.2-fold relative to bTiO2 alone and anatase, respectively, under simulated sunlight. Broad spectrum utilization and charge carrier separation enabled by bTiO2 and RGO, respectively, resulted in rapid generation of superoxide radial anions that, though less energetic than hydroxyl radicals generated by UV-excited anatase, are sufficient for degrading all but the most recalcitrant contaminants. The kinetic advantages of RGO/bTiO2 composites enable highly efficient removal of organic pollutants by exploiting the less energetic, but abundant, Vis-NIR spectrum.

Background and Objectives: Splenic hematoma is one of the common complications following abdominal blunt trauma, but non-traumatic splenic hematomas are not common and occurs in some cases, such as hematologic disorders and/or infections. Idiopathic splenic hematoma that occurs spontaneously without an underlying disease, is a rare occurrence, which its diagnosis needs strong clinical suspicion. Case report: The patient was a young man who referred to the emergency department with sustained lower abdominal pain during the past day. The pain aggravated by bending and was worsened by lying down. The patient reported no specific concomitant symptoms. His past medical history and drug history were negative. Chest x-ray and laboratory tests did not show any abnormal finding. In the upright radiograph of the abdomen, the shadow of the spleen seemed larger than the normal size. Abdominal sonography showed extensive subcutaneous hematoma of the spleen, which was confirmed by CT scan. The patient was under observation in the intensive care unit for 48 hours, that due to the pain intensification of the candidate, the splenectomy was performed. Pathologic study of splenic tissue was normal and he discharged within one week with satisfactory general health condition. &nbsp

Many recent works take advantage of highly parallel analog in-situ computation in memristor crossbars to accelerate the many vector-matrix multiplication operations in deep neural networks (DNNs). However, these in-situ accelerators have two significant shortcomings: The ADCs account for a large fraction of chip power and area, and these accelerators adopt a homogeneous design in which every resource is provisioned for the worst case. By addressing both problems, the new architecture, called Newton, moves closer to achieving optimal energy per neuron for crossbar accelerators. We introduce new techniques that apply at different levels of the tile hierarchy, some leveraging heterogeneity and others relying on divide-and-conquer numeric algorithms to reduce computations and ADC pressure. Finally, we place constraints on how a workload is mapped to tiles, thus helping reduce resource-provisioning in tiles. For many convolutional-neural-network (CNN) dataflows and structures, Newton achieves a 77-percent decrease in power, 51-percent improvement in energy-efficiency, and 2.1× higher throughput/area, relative to the state-of-the-art In-Situ Analog Arithmetic in Crossbars (ISAAC) accelerator.

Intel's SGX offers state-of-the-art security features, including confidentiality, integrity, and authentication (CIA) when accessing sensitive pages in memory. Sensitive pages are placed in an Enclave Page Cache (EPC) within the physical memory before they can be accessed by the processor. To control the overheads imposed by CIA guarantees, the EPC operates with a limited capacity (currently 128 MB). Because of this limited EPC size, sensitive pages must be frequently swapped between EPC and non-EPC regions in memory. A page swap is expensive (about 40K cycles) because it requires an OS system call, page copying, updates to integrity trees and metadata, etc. Our analysis shows that the paging overhead can slow the system on average by 5×, and other studies have reported even higher slowdowns for memory-intensive workloads. The paging overhead can be reduced by growing the size of the EPC to match the size of physical memory, while allowing the EPC to also accommodate non-sensitive pages. However, at least two important problems must be addressed to enable this growth in EPC: (i) the depth of the integrity tree and its cacheability must be improved to keep memory bandwidth overheads in check, (ii) the space overheads of integrity verification (tree and MACs) must be reduced. We achieve both goals by introducing a variable arity unified tree (VAULT) organization that is more compact and has lower depth. We further reduce the space overheads with techniques that combine MAC sharing and compression. With simulations, we show that the combination of our techniques can address most inefficiencies in SGX memory access and improve overall performance by 3.7×, relative to an SGX baseline, while incurring a memory capacity over-head of only 4.7%.

The COVID-19 pandemic has been particularly threatening to patients with end-stage kidney disease (ESKD) on intermittent hemodialysis and their care providers. Hemodialysis patients who receive life-sustaining medical therapy in healthcare settings, face unique challenges as they need to be at a dialysis unit three or more times a week, where they are confined to specific settings and tended to by dialysis nurses and staff with physical interaction and in close proximity. Despite the importance and critical situation of the dialysis units, modelling studies of the SARS-CoV-2 spread in these settings are very limited. In this paper, we have used a combination of discrete event and agent-based simulation models, to study the operations of a typical large dialysis unit and generate contact matrices to examine outbreak scenarios. We present the details of the contact matrix generation process and demonstrate how the simulation calculates a micro-scale contact matrix comprising the number and duration of contacts at a micro-scale time step. We have used the contacts matrix in an agent-based model to predict disease transmission under different scenarios. The results show that micro-simulation can be used to estimate contact matrices, which can be used effectively for disease modelling in dialysis and similar settings.

Historically, server designers have opted for simple memory systems by picking one of a few commoditized DDR memory products. We are already witnessing a major upheaval in the off-chip memory hierarchy, with the introduction of many new memory products—buffer-on-board, LRDIMM, HMC, HBM, and NVMs, to name a few. Given the plethora of choices, it is expected that different vendors will adopt different strategies for their high-capacity memory systems, often deviating from DDR standards and/or integrating new functionality within memory systems. These strategies will likely differ in their choice of interconnect and topology, with a significant fraction of memory energy being dissipated in I/O and data movement. To make the case for memory interconnect specialization, this paper makes three contributions. First, we design a tool that carefully models I/O power in the memory system, explores the design space, and gives the user the ability to define new types of memory interconnects/topologies. The tool is validated against SPICE models, and is integrated into version 7 of the popular CACTI package. Our analysis with the tool shows that several design parameters have a significant impact on I/O power. We then use the tool to help craft novel specialized memory system channels. We introduce a new relay-on-board chip that partitions a DDR channel into multiple cascaded channels. We show that this simple change to the channel topology can improve performance by 22% for DDR DRAM and lower cost by up to 65% for DDR DRAM. This new architecture does not require any changes to DIMMs, and it efficiently supports hybrid DRAM/NVM systems. Finally, as an example of a more disruptive architecture, we design a custom DIMM and parallel bus that moves away from the DDR3/DDR4 standards. To reduce energy and improve performance, the baseline data channel is split into three narrow parallel channels and the on-DIMM interconnects are operated at a lower frequency. In addition, this allows us to design a two-tier error protection strategy that reduces data transfers on the interconnect. This architecture yields a performance improvement of 18% and a memory power reduction of 23%. The cascaded channel and narrow channel architectures serve as case studies for the new tool and show the potential for benefit from re-organizing basic memory interconnects.

Achieving a smooth enamel surface after orthodontic bracket debonding is imperative. In this study, we sought to compare the enamel surface roughness values after orthodontic bracket debonding and resin removal using a white stone bur, a tungsten carbide bur, and a tungsten carbide bur under loupe magnification.Thirty sound premolars were randomly divided into 3 groups, and their buccal surfaces were subjected to atomic force microscopy to measure initial surface roughness. Brackets were bonded to the buccal surfaces and debonded after 24 hours. Resin remnants were removed using a white stone bur, a tungsten carbide bur, or a tungsten carbide bur under loupe magnification. The teeth were then subjected to atomic force microscopy again. The time required for composite removal was calculated. Data were analyzed using repeated-measures analysis of variance, 1-way analysis of variance, and the Tukey test.Resin removal increased the enamel surface roughness compared with the initial values (P 0.001); however, no significant differences were noted among the 3 groups in this respect after resin removal. The mean times required for smoothing by the tungsten carbide bur and the tungsten carbide bur with a dental loupe were similar (P 0.05): significantly lower than the time with the white stone bur (both, P 0.001).The tungsten carbide bur is still recommended for composite removal.

This paper presents one-dimensional ground response simulations of centrifuge models involving soft clay deposits subjected to ground motions of varying intensity. Total stress ground respo...

Policies, in general, determine the approach of interventions in society and, in turn, they are supposed to be influenced by theories. Theories are based on evidence and data; therefore, the methodology used to produce evidence and data plays a decisive influence on the final theory. In the field of addiction, addiction theories rely on the product of studies conducted on different individual, group, and environmental levels. Addiction, however, is a multi-dimensional (biological, psychological, and sociological) phenomenon. In this review article, we argue the pieces of evidence by which we build up a matrix structure in the theoretical model in order to provide a comprehensive understanding of this phenomenon. One axis of the matrix consists of the biological, psychological, and sociological dimensions of addiction, while the other axis, consists of individual, group, and environmental levels. We further discuss how such a multilevel and multi-dimensional approach does not exist in most addiction theories, and each of the theories has only explained single or limited elements of this matrix. This mono-level approach to the phenomenon of addiction can lead to major fallacies in the research and studies of the addiction.

Applications in the cloud are vulnerable to several attack scenarios. In one possibility, an untrusted cloud operator can examine addresses on the memory bus and use this information leak to violate privacy guarantees, even if data is encrypted. The Oblivious RAM (ORAM) construct was introduced to eliminate such information leak and these frameworks have seen many innovations in recent years. In spite of these innovations, the overhead associated with ORAM is very significant. This paper takes a step forward in reducing ORAM memory bandwidth overheads. We make the case that, similar to a cache hierarchy, a lightweight ORAM that fronts the full-fledged ORAM provides a boost in efficiency. The lightweight ORAM has a smaller capacity and smaller depth, and it can relax some of the many constraints imposed on the full-fledged ORAM. This yields a 2-level hierarchy with a relaxed ORAM and a full ORAM. The relaxed ORAM adopts design parameters that are optimized for efficiency and not capacity. We introduce a novel metadata management technique to further reduce the bandwidth for relaxed ORAM access. Relaxed ORAM accesses preserve the indistinguishability property and are equipped with an integrity verification system. Finally, to eliminate information leakage through LLC and relaxed ORAM hit rates, we introduce a deterministic memory scheduling policy. On a suite of memory-intensive applications, we show that the best Relaxed Hierarchical ORAM (ρ) model yields a performance improvement of 50%, relative to a Freecursive ORAM baseline.

This work reports the synthesis of g-C3N4, ZnSe and their nanocomposite for photocatalytic degradation of the congo red (CR) dye under visible-light irradiation. For the as-synthesized materials, their phase and morphology were confirmed by using powder X-ray diffraction (XRD) and transmission electron microscopy (TEM), respectively. In addition, other spectroscopic techniques including energy dispersive X-ray (EDX), X-ray photoelectron spectroscopy (XPS), UV-visible diffuse reflectance (DRS) and electron spin resonance (ESR) were also used to study their physiochemical and optoelectronic properties. Based on the valence band (VB) positions from XPS and bandgap energies from UV-visible DRS, alignment of energy levels vs. standard hydrogen electrode (SHE) was drawn which exhibited the formation of type-II heterostructure. The acquired degradation results reveal that the nanocomposite degrades 95.69% of the CR dye during 1 h of visible-light illumination, which is 1.57 and 1.81-folds higher than the degradation efficiency of bare ZnSe and g-C3N4, respectively. The promising results while using nanocomposite might be attributed to efficient interfacial charge transfer based on their type-II alignment. It has also been confirmed via ESR spectroscopy that the superoxide anion radical ( O2−) acts as the primary oxidant for the CR degradation. The photocatalyst reusability and sustainability have also been investigated.

Graphitic carbon nitride (g-C3N4) based photocatalysts have recently gained much attention for environmental remediation. However, the correlation between constituent counterparts in g-C3N4 based nanocomposites as well as mechanistic understanding related to the reactive oxygen species (ROS) have not been discussed much. Herein, ascribing to the significant thermodynamic potential of surface charge transfer between their conduction bands, we present spinel cobalt ferrite (CoFe2O4) decorated g-C3N4 nanosheets for photocatalytic mineralization of methylene blue (MB). The ROS are investigated using electron spin resonance (ESR) spectroscopy and the results confirm the formation of superoxide anion radical ( O 2 - ) as primary oxidant. Furthermore, UV–vis-NIR diffuse reflectance spectroscopy (DRS) and X-ray photoelectron spectroscopy (XPS) analysis are utilized to plot alignment of energy levels against standard hydrogen electrode (SHE). The band diagram does not only support the O 2 - formation but also provides interesting insights to explain the degradation dynamics based on the respective band positions in the nanocomposite.

In recent years, the number of cyber-physical incidents in industrial control systems (ICSs) has increased. Providing a framework for ICS threat intelligence is of utmost importance because of the critical role of ICSs in the nations' critical infrastructures. In this paper, after a short review of various threats and security incidents’ taxonomies in the cyber-physical scope, we propose the Hierarchical Taxonomic Framework (HTF) with required characteristics for classifying attacks and security incidents in ICSs. We applied the HTF to analyze 268 available public security incidents on ICSs reported between 1982 and 2018. Among these 268 incidents, there are 147 attacks and 121 non-attack security incidents. The HTF and the analytical incidents study are carried out to extract the useful patterns and key points for organizing threat intelligence in ICSs and critical infrastructures to improve their security level according to the cyber-attacks trends.

In this work, a mathematical model was developed to simulate the urinary concentration mechanism. A 3-D geometry was derived based on the detail physiological pictures of rat kidney. The approximate region of each tubule was obtained from the volume distribution of structures based on Walter Pfaller's monograph and Layton's region-based model. Mass and momentum balances were applied to solve for the change in solutes concentration and osmolality. The osmolality of short and long descending nephrons at the end of the outer medulla was obtained to be 530 mOsmol/kgH2O and 802 mOsmol/kgH2O, respectively, which were in acceptable agreement with experimental data. The fluid osmolality of the short and long ascending nephrons was also compatible with experimental data. The osmolality of CD fluid at the end of the inner medulla was determined to be 1198 mOsmol/kgH2O which was close the experimental data (1216 ± 118). Finally, the impact of the position of each tubule on the fluid osmolality and solutes concentration were obvious in the results; for example, short descending limb a1, which is the closest tubule to the collecting duct, had the highest urea concentration in all tubules. This reflects the important effect of the 3D modeling on the precise analysis of urinary concentration mechanism.

Timing channels can be exploited to leak information between two virtual machines running on a shared server. Indeed, cache timing channels are important components in the Spectre attack. A shared memory controller can also be leveraged to establish a timing channel. Recent efforts have designed memory controllers that eliminate such timing channels, but that incur throughput penalties of over 2X. This paper advances the state-of-the-art by better matching the memory controller policies to the memory-level-parallelism (MLP) needs of typical applications. Our new memory controller improves upon the performance of the state-of-the-art policy by 14%, while eliminating memory controller timing channels.

Purpose: Intermittent fasting and curtailing water intake for extended periods were likely common in Paleolithic times. Today it occurs for religious and dietary reasons. This restriction in intake should cause a decrease in the urine flow rate while raising the concentration of certain substances in urine to the point of precipitation. In this study we measured the risk of CaHPO4 precipitation following 18 hours of food and water deprivation.Materials and Methods: Urine samples were periodically collected from 15 healthy subjects who fasted and abstained from drinking any liquid for 18 hours. The urine constituents Ca2+, HPO42– and pH involved in CaHPO4 formation were measured at various times throughout the fasting day. A comparison was made with control data, which consisted of diurnal urine collections taken throughout a separate nonfasting day prior to the fasting day.Results: The mean ± SEM urine flow rate decreased significantly from 0.93 ± 0.1 ml per minute in the control group to 0.37 ± 0.05 ml p...

As more critical applications move to the cloud, there is a pressing need to provide privacy guarantees for data and computation. While cloud infrastructures are vulnerable to a variety of attacks, in this work, we focus on an attack model where an untrusted cloud operator has physical access to the server and can monitor the signals emerging from the processor socket. Even if data packets are encrypted, the sequence of addresses touched by the program serves as an information side channel. To eliminate this side channel, Oblivious RAM constructs have been investigated for decades, but continue to pose large overheads. In this work, we make the case that ORAM overheads can be significantly reduced by moving some ORAM functionality into the memory system. We first design a secure DIMM (or SDIMM) that uses commodity low-cost memory and an ASIC as a secure buffer chip. We then design two new ORAM protocols that leverage SDIMMs to reduce bandwidth, latency, and energy per ORAM access. In both protocols, each SDIMM is responsible for part of the ORAM tree. Each SDIMM performs a number of ORAM operations that are not visible to the main memory channel. By having many SDIMMs in the system, we are able to achieve highly parallel ORAM operations. The main memory channel uses its bandwidth primarily to service blocks requested by the CPU, and to perform a small subset of the many shuffle operations required by conventional ORAM. The new protocols guarantee the same obliviousness properties as Path ORAM. On a set of memory-intensive workloads, our two new ORAM protocols – Independent ORAM and Split ORAM – are able to improve performance by 1.9x and energy by 2.55x, compared to Freecursive ORAM.

This study presents a new model for probabilistic seismic hazard assessment based on fuzzy sets theory. To accomplish seismic hazard analysis in the framework of fuzzy sets theory, all of the variables are first converted into Gaussian fuzzy sets using α-cut method. Then, fuzzified variables are used in seismic hazard analysis. Finally, the outputs are defuzzified using center of area method. The method is applied to Tehran region, Iran, and the fuzzy hazard curve is obtained for the site. The curve displays a fuzzy-probabilistic estimate of peak ground acceleration (PGA) over bedrock for the various return periods. PGA values for the region are estimated to be 0.18–0.20 g and 0.42–0.48 g for 50- and 475-year return periods, respectively. The results are given as fuzzy intervals which accommodate the vagueness inherent in the data.

In recent years, multiple neuromorphic architectures have been designed to execute cognitive applications that deal with image and speech analysis. These architectures have followed one of two approaches. One class of architectures is based on machine learning with artificial neural networks. A second class is focused on emulating biology with spiking neuron models, in an attempt to eventually approach the brain's accuracy and energy efficiency. A prominent example of the second class is IBM's TrueNorth processor that can execute large spiking networks on a low-power tiled architecture, and achieve high accuracy on a variety of tasks. However, as we show in this work, there are many inefficiencies in the TrueNorth design. We propose a new architecture, INXS, for spiking neural networks that improves upon the computational efficiency and energy efficiency of the TrueNorth design by 3,129× and 10× respectively. The architecture uses memristor crossbars to compute the effects of input spikes on several neurons in parallel. Digital units are then used to update neuron state. We show that the parallelism offered by crossbars is critical in achieving high throughput and energy efficiency.