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Understanding the mechanisms and controlling
the possibilities of surface nanostructuring is of crucial interest
for both fundamental science and application perspectives.
Here, we report a direct experimental observation
of laser-induced periodic surface structures (LIPSS) formed
near a predesigned gold step edge following single-pulse
femtosecond laser irradiation. Simulation results based on a
hybrid atomistic-continuum model fully support the experimental
observations. We experimentally detect nanosized
surface features with a periodicity of ∼300 nm and heights of
a few tens of nanometers.We identify two key components of
single-pulse LIPSS formation: excitation of surface plasmon
polaritons and material reorganization. Our results lay a
solid foundation toward simple and efficient usage of light
for innovative material processing technologies.
Grinding is one of the effective manufacturing processes with which to produce highly accurate parts with an ultra-fine surface finish. The tool used to remove materials in grinding is called the grinding wheel. Abrasive grains made of extremely hard materials (alumina, silica, cubic boron nitride, and diamond) having a definite grit size but a random shape are bonded on the circumferential surface of the grinding wheel. The fabrication process is controlled so that the wheel exhibits a prescribed structure (in the scale of soft to hard). At the same time, the distribution of grains must follow a prescribed grade (in the scale of dense to open). After the fabrication, the wheel is dressed to make sure of its material removal effectiveness, which itself depends on the surface topography. The topography is quantified by the distribution and density of active abrasive grains located on the circumferential surface, the grains’ protrusion heights, and their pore volume ratio. The prediction of the surface topography mentioned above requires a model that considers the entire manufacturing process and the influences on the grinding wheel properties. This study fills this gap in modelling the grinding wheel by presenting a surface topography model and simulation framework for the effect of the grinding wheel fabrication process on the surface topography. The simulation results have been verified by conducting experiments. This study will thus help grinding wheel manufacturers in developing more effective grinding wheels.
This paper aims to improve the traditional calibration method for reconfigurable self-X (self-calibration, self-healing, self-optimize, etc.) sensor interface readout circuit for industry 4.0. A cost-effective test stimulus is applied to the device under test, and the transient response of the system is analyzed to correlate the circuit's characteristics parameters. Due to complexity in the search and objective space of the smart sensory electronics, a novel experience replay particle swarm optimization (ERPSO) algorithm is being proposed and proved a better-searching capability than some currently well-known PSO algorithms. The newly proposed ERPSO expanded the selection producer of the classical PSO by introducing an experience replay buffer (ERB) intending to reduce the probability of trapping into the local minima. The ERB reflects the archive of previously visited global best particles, while its selection is based upon an adaptive epsilon greedy method in the velocity updating model. The performance of the proposed ERPSO algorithm is verified by using eight different popular benchmarking functions. Furthermore, an extrinsic evaluation of the ERPSO algorithm is also examined on a reconfigurable wide swing indirect current-feedback instrumentation amplifier (CFIA). For the later test, we proposed an efficient optimization procedure by using total harmonic distortion analyses of CFIA output to reduce the total number of measurements and save considerable optimization time and cost. The proposed optimization methodology is roughly 3 times faster than the classical optimization process. The circuit is implemented by using Cadence design tools and CMOS 0.35 µm technology from Austria Microsystems (AMS). The efficiency and robustness are the key features of the proposed methodology toward implementing reliable sensory electronic systems for industry 4.0 applications.
The scaffolding protein family Fe65, composed of Fe65, Fe65L1, and Fe65L2, was identified as an interaction partner of the amyloid precursor protein (APP), which plays a key function in Alzheimer’s disease. All three Fe65 family members possess three highly conserved interaction domains, forming complexes with diverse binding partners that can be assigned to different cellular functions, such as transactivation of genes in the nucleus, modulation of calcium homeostasis and lipid metabolism, and regulation of the actin cytoskeleton. In this article, we rule out putative new intracellular signaling mechanisms of the APP-interacting protein Fe65 in the regulation of actin cytoskeleton dynamics in the context of various neuronal functions, such as cell migration, neurite outgrowth, and synaptic plasticity.
This review aims to provide a concise overview of the role of (digital) data and new data practices in schools. By focusing on the impact of data on pedagogical practices, it aims to shed light on how the everyday tasks of teachers and other pedagogical staff in schools are changing, particularly as a result of the generation and use of digital data. For this purpose, existing studies and previous theoretical debates on this topic are examined for their perspectives on data and data practices in schools. The pedagogical data practices of (improving) teaching and learning, assessment and counseling, (data-driven) decision-making, and cooperation and collaboration by “doing data” will be elaborated and discussed. Likewise, data practices that are missing from the studies are identified. We conclude with an overview of blind spots and further research needs.
Bees are recognized as an indispensable link in the human food chain and general ecological system.
Numerous threats, from pesticides to parasites, endanger bees and frequently lead to hive collapse. The varroa destructor mite is a key threat to bee keeping and the monitoring of hive infestation level is of major concern for effective treatment. Sensors and automation, e.g., as in condition-monitoring and Industry 4.0, with machine
learning offer help. In numerous activities a rich variety of sensors have been applied to apiary/hive
instrumentation and bee monitoring. Quite recent activities try to extract estimates of varroa infestation level by
hive air analysis based on gas sensing and gas sensor systems. In our work in the IndusBee4.0 project [8, 11], an hive-integrated, compact autonomous gas sensing system for varroa infestation level estimation based on low-
cost highly integrated gas sensors was conceived and applied. This paper adds to [11] with the first results of a
mid-term duration investigation from July to September 2020 until formic acid treatment. For the regarded hive more than 79 % of detection probability based on the SGP30 gas sensor readings have been achieved.
The handling of oxygen sensitive samples and growth of obligate anaerobic organisms
requires the stringent exclusion of oxygen, which is omnipresent in our normal atmospheric
environment. Anaerobic workstations (aka. Glove boxes) enable the handling of
oxygen sensitive samples during complex procedures, or the long-term incubation of
anaerobic organisms. Depending on the application requirements, commercial workstations
can cost up to 60.000 €. Here we present the complete build instructions for a highly
adaptive, Arduino based, anaerobic workstation for microbial cultivation and sample handling,
with features normally found only in high cost commercial solutions. This build can
automatically regulate humidity, H2 levels (as oxygen reductant), log the environmental
data and purge the airlock. It is built as compact as possible to allow it to fit into regular
growth chambers for full environmental control. In our experiments, oxygen levels during
the continuous growth of oxygen producing cyanobacteria, stayed under 0.03 % for 21 days
without needing user intervention. The modular Arduino controller allows for the easy
incorporation of additional regulation parameters, such as CO2 concentration or air pressure.
This paper provides researchers with a low cost, entry level workstation for anaerobic
sample handling with the flexibility to match their specific experimental needs.
Climate-Based Analysis for the Potential Use of Coconut Oil as Phase Change Material in Buildings
(2021)
One of the most efficient measures to reduce energy consumption in buildings is using
passive thermal comfort strategies. This paper shows the potential of coconut oil as a bio-based
phase change material (PCM) incorporated into construction components to improve the thermal
performance of buildings for several climates, due to its environmental advantages, wide availability,
and economic feasibility. The thermophysical properties of coconut oil were determined through
differential scanning calorimetry. Numerical simulations were conducted in ESP-r, comparing an
office space with a gypsum ceiling to one with coconut oil as PCM for 12 climate types in the Köppen–
Geiger classification. The results show that coconut oil is a suitable PCM for construction applications
under tropical and subtropical climates. This PCM can provide year-round benefits for these climates,
even though a higher melting point is needed for optimum performance during hotter months. The
highest demand reduction of 32% and a maximum temperature reduction of 3.7 °C were found in
Mansa, Zambia (Cwa climate). The best results occur when average outdoor temperatures are within
the temperature range of phase change. The higher the diurnal temperature range, the better the
results. Our findings contribute to a better understanding of coconut oil in terms of its properties
and potential for application in the building sector as PCM.
In past decades, many cities and regions have underwent structural transformations—e.g.,
in old industrialized “rust belts” or in peripheral rural areas. Many of these shrinking cities have
to face the challenges of long-term demographic and economic changes. While shrinkage is often
related to post-industrial transformations in the USA, in other countries, such as Germany, for
example, the causes are related to changing demographics with declining birth rates and the effects
of the German reunification. Many cities have tried to combat shrinkage and have thus developed
a variety of policies and strategies such as the establishing of substitute industries. To assess the
sustainability of this approach, this paper investigates the cities of Cleveland, USA and Bochum,
Germany in a comparative analysis following the most similar/most different research design. The
paper shows that substitute industries might lead to new development paths for shrinking cities.
Daylight is important for the well-being of humans. Therefore, many office buildings use
large windows and glass facades to let more daylight into office spaces. However, this increases the
chance of glare in office spaces, which results in visual discomfort. Shading systems in buildings
can prevent glare but are not effectively adapted to changing sky conditions and sun position,
thus losing valuable daylight. Moreover, many shading systems are also aesthetically unappealing.
Electrochromic (EC) glass in this regard might be a better alternative, due to its light transmission
properties that can be altered when a voltage is applied. EC glass facilitates zoning and also supports
control of each zone separately. This allows the right amount of daylight at any time of the day.
However, an effective control strategy is still required to efficiently control EC glass. Reinforcement
learning (RL) is a promising control strategy that can learn from rewards and penalties and use this
feedback to adapt to user inputs. We trained a Deep Q learning (DQN) agent on a set of weather data
and visual comfort data, where the agent tries to adapt to the occupant’s feedback while observing
the sun position and radiation at given intervals. The trained DQN agent can avoid bright daylight
and glare scenarios in 97% of the cases and increases the amount of useful daylight up to 90%, thus
significantly reducing the need for artificial lighting.