Specific features of Eu~(3+) and Tb~(3+) magnetooptics in gadolinium-gallium garnet (Gd_3Ga_5O_(12))

We reported magnetooptical properties of Eu3+(4f(6)) and Tb3+(4f(8)) in single crystals of Gd3Ga5O12 (GGG), Y3Ga5O12 (YGG), and Eu3+(4f(6)) in Eu3Ga5O12 (EuGG) for both ions occupying sites of D2 symmetry in the garnet structure. Absorption, luminescence, and magnetic circular polarization of luminescence (MCPL) spectra of Tb3+ in GGG and YGG and absorption and magnetic circular dichroism (MCD) of Eu3+ in EuGG were studied. The data were obtained at 85 K and room temperature (RT). Magnetic susceptibility of...     

Influence of carbon nanotube (CNT) on the mechanical properties of LLDPE/CNT nanocomposite fibers

The present study shows the effect of adding CNT to linear low-density polyethylene (LLDPE) to produce LLDPE/CNT nanocomposite fibers. The LLDPE/CNT fibers were produced by melt extrusion process using a twin-screw extruder, in a controlled temperature from 160 °C to 275 °C. Further, melt extrusion process was followed by drawing of fibers at the room temperature. Three different weight percentages, 0.08, 0.3 and 1 wt.% of CNT were studied for producing nanocomposite fibers. The addition of 1 wt.% CNT in the LLDPE fiber has increased the tensile strength by 38% (350 MPa). The addition of 0.08 and 0.3 wt.% CNT in the fiber matrix has improved the ductility by 87% and 122%, respectively. Similarly, improvement in the toughness was observed by 63% and 105% for LLDPE fibers with 0.08 wt.% and 0.3 wt.% CNT respectively. The increase in the mechanical properties of the composite fibers was attributed to the alignment and distribution of CNT in the LLDPE matrix. The dispersion of CNT in the polymeric matrix has been revealed by SEM. The study shows that the small addition of CNT when properly mixed and aligned will increase the mechanical properties of pristine polymer fibers.     

Immunohistochemical analysis of S6K1 and S6K2 expression in human breast tumors

AIM: To express recombinant S6K2 in baculovirus expression system; to purify large quantities of recombinant S6K2 for biochemical studies; to generate and characterise specific MABs against recombinant S6K2; to study the patterns S6K1 and S6K2 expression and subcellular localization in normal, benign and malignant breast tissues. METHODS: Recombinant baculovirus, expressing wild type S6K2 was generated using Bac-to-Bac system (Invitrogen); recombinant S6K was purified from infected Sf9 cells using affinity purification approach; monoclonal antibodies against recombinant S6K2 were generated; the specificity of generated MABs towards recombinant and endogenous S6K2 were examined by ELISA, Western blotting, immunoprecipitation and immuhohistochemical staining; immunohistochemical detection of S6K1 and S6K2 in human breast tissues was performed using specific monoclonal antibodies towards S6K1 and S6K2. RESULTS: Large amounts of enzymatically active S6K2 were purified using baculovirus expression system; highly purified preparations of S6K2 were used to generate and characterize anti-S6K2 MABs; elevated levels of S6K1 and S6K2 were found in breast tumors when compared to normal breast tissues; S6K2 is frequently localized in the nuclei of adenocarcinoma tissues, but rarely in fibroadenoma or "normal" breast tissues. CONCLUSION: Production of recombinant S6K2 in large amount and generation of specific monoclonal antibodies towards S6K2 has provided us with excellent tools to study the function and regulation of this important signalling molecule in normal and cancer cells. Immunnohistochemical analysis of S6K1 and S6K2 expression in normal and malignant breast clearly indicates that both kinases are overexpressed in breast tumors, when compared to "normal" tissues. The retention of S6K2 in the nuclei of malignant cells may be caused by disregulation of nucleocytoplasmic shuttling and could subsequently affect cell growth and proliferation.     

Nitric oxide synthase inhibitors do not attenuate diacylglycerol or monoacylglycerol lipase activities in synaptoneurosomes

Neuron-enriched cultures and synaptoneurosomal fractions from 10 day-old rat brain contain diacylglycerol and monoacylglycerol lipase activities. Glutamate and its analogs stimulate the activities of diacylglycerol and monoacylglycerol lipases in a time- and dose-dependent manner. Stimulation of diacylglycerol and monoacylglycerol lipases by glutamate or NMDA can be blocked by MK-801 (non-competitive antagonist). Nitro L-arginine methyl ester and L-methylarginine have no effect on glutamate stimulated activities of diacylglycerol and monoacylglycerol lipases. Our studies suggest that synaptoneurosomal preparations from young rat brain are useful for obtaining important information on signal transduction.     

掺杂三乙醇胺铜配合物的PVA复合薄膜/K+交换玻璃光波导器件检测SO2气体

三乙醇胺酮配合物气敏性研究发现,该配合物与二氧化硫气体作用时透光率发生变化。以此结果为依据在K 交换玻璃光波导表面固定三乙醇胺铜配合物掺杂的聚乙烯醇复合薄膜,研制出了二氧化硫气体传感元件,并在光波导传感检测系统中测定其传感特性。实验结果表明,本传感元件对低浓度(500×10-9～15×10-6)二氧化硫气体有良好的线性快速可逆响应,低浓度氯化氢、硫化氢、二氧化氮、氨气以及挥发性有机物蒸汽对二氧化硫气体的检测没有干扰,具有灵敏度和选择性高、可逆性好,结构简单和易制作等特点。     

Community land trusts: releasing possible selves through stable affordable housing

Housing affordability – a long-standing issue for low-income households – is crucial for the flourishing of both households and communities. When housing is unaffordable, households struggle to attain and maintain housing, which negatively effects household well-being. Since the foreclosure crisis, community land trusts (CLTs) have emerged as a viable housing policy. Relying on quantitative and qualitative data collected by a Minneapolis-based CLT, this study examines the experiences of 91 CLT homeowners. Our analysis illustrates how the CLT’s institutional framework alters the political, economic, social and material relations that characterize the lives of these households to facilitate the provision of previously unavailable resources. Beyond indefinitely stabilizing households, this new arrangement of relations creates a foundation for the cultivation of ontological security and contributes to the opening up of possibilities and the unfolding of life in ways not previously possible.     

Enhancing energy efficiency in the residential sector with smart meter data analytics

Tailored energy efficiency campaigns that make use of household-specific information can trigger substantial energy savings in the residential sector. The information required for such campaigns, however, is often missing. We show that utility companies can extract that information from smart meter data using machine learning. We derive 133 features from smart meter and weather data and use the Random Forest classifier that allows us to recognize 19 household classes related to 11 household characteristics (e.g., electric heating, size of dwelling) with an accuracy of up to 95% (69% on average). The results indicate that even datasets with an hourly or daily resolution are sufficient to impute key household characteristics with decent accuracy and that data from different yearly seasons does not considerably influence the classification performance. Furthermore, we demonstrate that a small training data set consisting of only 200 households already reaches a good performance. Our work may serve as benchmark for upcoming, similar research on smart meter data and provide guidance for practitioners for estimating the efforts of implementing such analytics solutions.     

Performance Assessment of Construction Engineering and Management (CEM) Degree Program in Developing Countries: Case of Pakistan

The performance of construction industry is largely supported by the competence and skills generated by Construction Engineering and Management (CEM) programs offered in the country. A review of published literature points to scarcity of studies evaluating performance of curricula with respect to generating the requisite skills. In an attempt to assess the efficiency of CEM programs offered in Pakistan, this study identifies the key knowledge areas, technical skills, and expertise that these programs need to focus on for sufficiently preparing the post graduating students entering modern and complex construction industry. In doing so, two universities (NEDUET and NUST) offering mature CEM program at postgraduate level have been engaged. Based on a questionnaire survey of CEM graduates, employers and academicians, it is found that the program content is adequately designed and delivered by well versed and competent instructors. Generally, a high degree of agreement for technical skills is found among the perceptions of graduates and the expectations of the industry. However, some major challenges are identified which if addressed can help boost the satisfaction level of students. Acknowledging the possibility for improvement, recommendations for curricula updates in order to bridge the gap between academia and industry are proposed.     

Self‐Powered Sensors Enabled by Wide‐Bandgap Perovskite Indoor Photovoltaic Cells

A new approach to ubiquitous sensing for indoor applications is presented, using low‐cost indoor perovskite photovoltaic cells as external power sources for backscatter sensors. Wide‐bandgap perovskite photovoltaic cells for indoor light energy harvesting are presented with the 1.63 and 1.84 eV devices that demonstrate efficiencies of 21% and 18.5%, respectively, under indoor compact fluorescent lighting, with a champion open‐circuit voltage of 0.95 V in a 1.84 eV cell under a light intensity of 0.16 mW cm^?2. Subsequently, a wireless temperature sensor self‐powered by a perovskite indoor light‐harvesting module is demonstrated. Three perovskite photovoltaic cells are connected in series to create a module that produces 14.5 µW output power under 0.16 mW cm^?2 of compact fluorescent illumination with an efficiency of 13.2%. This module is used as an external power source for a battery‐assisted radio‐frequency identification temperature sensor and demonstrates a read range by of 5.1 m while maintaining very high frequency measurements every 1.24 s. The combined indoor perovskite photovoltaic modules and backscatter radio‐frequency sensors are further discussed as a route to ubiquitous sensing in buildings given their potential to be manufactured in an integrated manner at very low cost, their lack of a need for battery replacement, and the high frequency data collection possible.     

A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay in the queues, the vehicles experience at different road junctions across the city. The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things (IoT) sensors across the road. After due preprocessing over the cloud server, the proposed approach makes use of this data by incorporating the neuro-fuzzy engine. Consequently, it possesses a high level of accuracy by means of intelligent decision making with minimum error rate. Simulation results reveal the accuracy of the proposed model as 98.72% during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%, 95.84%, 97.56% and 98.03%, respectively. As far as the training phase analysis is concerned, the proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment.