Previous Value Research Articles

Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p<0.05) indicating that the previous day value of the stochastic term also had a significant effect on the present value of meteorological parameters in the environment. It was observed that in all the fitted models except for wind direction in Conakry and rainfall in Abidjan have all their Autoregressive term of order 1 significant (p<0.05) which implies that previous day value of these meteorological parameter had a significant effect on the present day value of the parameters. Therefore, the forecast model indicates that maximum temperature are expected in February, March, April, and June while minimum temperatures in January, August, December. Although, the selected models cannot forecast the precise air temperature, this can also provide information that can be of help to create tactics for appropriate preparation of farming which can be used as tools for effective envi­ronmental preparation and policymaking.

Direct determination of the excitation energy of the quasistable isomer Ta180m

$^{180m}\mathrm{Ta}$ is a naturally abundant quasistable nuclide and the longest-lived nuclear isomer known to date. It is of interest, among others, for the search for dark matter, for the development of a $\ensuremath{\gamma}$ laser, and for astrophysics. So far, its excitation energy has not been measured directly but has been based on an evaluation of available nuclear reaction data. We have determined the excitation energy of this isomer with high accuracy using the Penning-trap mass spectrometer JYFLTRAP. The determined mass difference between the ground and isomeric states of $^{180}\mathrm{Ta}$ yields an excitation energy of 76.79(55) keV for $^{180m}\mathrm{Ta}$. This is the first direct measurement of the excitation energy and provides a better accuracy than that of the previous evaluation value, 75.3(14) keV.

Surveillance Capitalism as a Form of Biopower in Historical Perspective

Information has become the most important commodity of markets since the Oil Crisis of 1973. This period is also called as New Economy in which industry lost its previous value while services using the new information technologies are put in the core of economic activities. The rise of information technologies has brought forth new ways to generate profits. IT companies have directed their marketing interests to generate profits in behavioral forecasting models through which future behaviors of individuals can be predicted by means of machine intelligence. Accordingly, since the capital accumulation of the new stage is based on the surveillance of individuals, Shoshana Zuboff called this new stage as “surveillance capitalism”. This shift in the market interests can be understood within its historical context in which the management strategies were already being shifted from a sociological bureaucracy to new Taylorist scientific management based on continual surveillance. Thus, surveillance capitalism can be interpreted as a new “technology of the self” aiming at subordination of individuals through psychological manipulations within neoliberal governmentality. This article aims to put forth the historical link between the emergence of surveillance capitalism and the use of individualized management strategies as a new form of capitalistic biopower.

Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts

HIV treatment programs face challenges in identifying patients at risk for loss-to-follow-up and uncontrolled viremia. We applied predictive machine learning algorithms to anonymised, patient-level HIV programmatic data from two districts in South Africa, 2016–2018. We developed patient risk scores for two outcomes: (1) visit attendance ≤ 28 days of the next scheduled clinic visit and (2) suppression of the next HIV viral load (VL). Demographic, clinical, behavioral and laboratory data were investigated in multiple models as predictor variables of attending the next scheduled visit and VL results at the next test. Three classification algorithms (logistical regression, random forest and AdaBoost) were evaluated for building predictive models. Data were randomly sampled on a 70/30 split into a training and test set. The training set included a balanced set of positive and negative examples from which the classification algorithm could learn. The predictor variable data from the unseen test set were given to the model, and each predicted outcome was scored against known outcomes. Finally, we estimated performance metrics for each model in terms of sensitivity, specificity, positive and negative predictive value and area under the curve (AUC). In total, 445,636 patients were included in the retention model and 363,977 in the VL model. The predictive metric (AUC) ranged from 0.69 for attendance at the next scheduled visit to 0.76 for VL suppression, suggesting that the model correctly classified whether a scheduled visit would be attended in 2 of 3 patients and whether the VL result at the next test would be suppressed in approximately 3 of 4 patients. Variables that were important predictors of both outcomes included prior late visits, number of prior VL tests, time since their last visit, number of visits on their current regimen, age, and treatment duration. For retention, the number of visits at the current facility and the details of the next appointment date were also predictors, while for VL suppression, other predictors included the range of the previous VL value. Machine learning can identify HIV patients at risk for disengagement and unsuppressed VL. Predictive modeling can improve the targeting of interventions through differentiated models of care before patients disengage from treatment programmes, increasing cost-effectiveness and improving patient outcomes.

Demonstration of Temperature-Dependent Analysis of GAA – β-(AlGa)2O3/Ga2O3 High Electron Mobility Transistor

In this work, we have, designed and demonstrated Gate all around (GAA)-β-AGO/GO HEMT to achieve large current density, electron mobility and electron velocity at cryogenic temperatures. Because the controlling of the gate is better in GAA-based structures the performance of the proposed device is magnified for a different temperature range. The 2DEG channel mobility of the proposed device is boosted from 130 cm2/Vs at 300 K to 4100 cm2/Vs at 50 K. Mobility of the proposed device is boosted from the previous value by the factor of 2.5 at room temperature. The higher electron mobility at lower temperatures made it suitable to reach a larger saturation velocity center of the channel. Analog, RF and linearity parameters are analyzed with different temperature ranges at a gate length of 0.9 µm. The drain current and cutoff frequency of the device is 750 mA/mm and 18 GHz, respectively at 50 K and it reduces for a larger temperature. The breakdown voltage of the proposed device at 300 K is 139 V. The noise analysis is also taken for the investigation of its random nature. This type of device is used in power-switching applications.

Biological Assessment of Potential Exposure to Occupational Substances in Current Semiconductor Workers with at Least 5 Years of Employment.

Background: this study aimed to conduct a biological assessment of the potential exposure to carcinogenic substances in current semiconductor workers. Methods: A cross-sectional study was conducted on 306 semiconductor workers. The assessed biomarkers were as follows: (benzene) urine S-phenylmercapturic, trans,trans-muconic acid, blood benzene; (trichloroethylene) urine trichloroacetic acid; (2-ethoxyethanol) 2-ethoxyacetic acid; (arsine) urine arsenic3+, arsenic5+, monomethylarsonic, dimethylarsinic acid, arsenobetaine; (shift work) 6-hydroxymelatonin; (smoking) cotinine, and (radiation). The detection rate of these materials is defined as more than the biological exposure index (BEI) or the previous reference value. Results: Some workers exposed to trans,trans-muconic acid, trichloroacetic acid, and arsenic5+ showed high BEI levels. Generally, there was no difference according to job categories, and workers were suspected to be exposed to other sources. The melatonin concentration tended to decrease when working at night, and cotinine was identified as an excellent surrogate marker for smoking. In the case of radiation exposure, there was no significant difference in the number of stable chromosome translocation in 19 semiconductor workers. Their estimated radiation exposure level was below the limit of detection (LOD) or near the LOD level. Conclusion: In this study, most carcinogens were below the BEI level, but verification through re-measurement was needed for workers who were identified to have a high BEI level. For continuous monitoring, a prospective cohort is necessary to deal with the healthy worker effect and assess additional materials.

Adoption of ICRU report 90 recommendations in the Canadian Co-60 air-kerma primary standard

The Canadian air-kerma primary standard for 60Co beams is updated according to ICRU report 90 (ICRU-90) recommendations. The effect of these recommendations and a more detailed chamber model on the primary standard is investigated. Dosimetric quantities and corrections required for the realization of air kerma are calculated using the EGSnrc Monte Carlo (MC) simulation system. The validity of Spencer–Attix (SA) cavity theory as function of Δ, the secondary particle production threshold energy, is investigated to assess the accuracy of its current selection criterion. Although individual changes in some correction factors are statistically non-negligible, the overall effect amounts to only a 0.04% increase of the standard. The mean restricted mass electronic stopping power ratio graphite to air, decreases from the previous value of 1.0010 to a value of 0.9949. If this value is used with the recommended W air value of 33.97 eV, the effect on the standard is a reduction of 0.613%. When the recommended value of 33.72 eV is used instead, the change results in a 0.835% reduction in K air. Using SA cavity theory to obtain K air with a Δ value of 20 keV, corresponding to the cavity’s mean chord length, reproduces a direct MC calculation at the 0.03% level. Type B uncertainties due to uncertainty in the photon and electron cross sections for the largest corrections K wall and K comp are estimated to be 0.01% and 0.10%, respectively. No statistically significant effect on the standard is observed due to changes in the correction factors. Fano tests and comparison to single scattering calculations demonstrate EGSnrc’s electron transport algorithm accuracy to be within 0.03% relative to its own cross sections. The latter also validates the criterion for selecting Δ based on the average chord length of the chamber’s cavity. A 0.2% difference in the restricted stopping power ratio obtained in this work and the value resulting from ICRU-90 recommendations has the potential for affecting standards relying on the absolute value of W air, thus further investigation is granted. The current update results in a 0.8% decrease of the Canadian 60Co air-kerma primary standard with a reduced uncertainty of 0.2%. This change is in excellent agreement with the reported change to the BIPM 60Co air-kerma standard.

Evolution of beryllium safety standards over the last 70 years and challenges ahead

Beryllium (Be), its alloys, and ceramics are widely used in high-tech applications such as electronics, space, atomic energy, and other day-to-day items of use. Initially, Be-based phosphors such as ZnBeSiO4 were being used in the lamp industry during the 1930s onward but were soon abandoned due to lung-related diseases and deaths of workers in the phosphor industry which was attributed to the highly toxic nature of Be. Typical effects associated with Be inhalation are chronic and acute Be diseases (CBD and ABD) and the main target organ being affected is the lung although effects on other human body organs are also well documented. Such diseases were observed not only in occupational workers handling Be but also in the members of the public residing in the neighborhood of Be manufacturing and processing facilities, especially in the USA. The CBD in occupational workers may depend on many factors such as individual's sensitivity to Be, amount of Be exposure, nature of Be compound, and types of Be operations and processes being performed. All this led to safety concerns about the toxicity of Be and recommendations regarding Be air concentration in the workplace and public environment were issued by the Department of Energy, USA in 1949 as occupational exposure limit (OEL)/threshold limit values (TLVs) which were 2.0 μg/m3 and 0.01 μg/m3 for occupational Be workers and public environment, respectively. It is worth to mention that these recommendations were adopted by various countries and organizations either as it is or with small changes. Later, different organizations recommended changes in the value of TLV for occupational workplaces, but such changes were never adopted as they were lacking sound epidemiological basis. The OEL/TLV of 2.0 μg/m3 continued for nearly 70 years until Occupational Safety and Health Administration (OSHA) in 2017 reduced the Be air concentration (BeAir-Conc) limit for occupational workers to 0.20 μg/m3 as the value of 2.00 μg/m3 was inadequate to protect occupational workers from CBD. This is a major change in the Be-related safety standards recommended recently and is/being adopted by many countries. The present article provides details about the evolution of Be safety standards over the last 70 years, the notion behind the recent revision of Be permissible exposure limit (PEL) value from 2.0 μg/m3 to 0.20 μg/m3 by OSHA and the associated safety challenges ahead. The information from literature about Be safety and related safety standards adopted in India is also given. The article also provides details about TLVs for BeAir-Conc being followed in various countries in the world and various challenges for the implementation of a revised PEL value of 0.20 μg/m3 as suggested by OSHA i.e., reduction in PEL value by a factor of 10 or recommendation of revised TLV of 0.05 μg/m3 by American Conference of Governmental Industrial Hygienists as compared to the previous value of 2.0 μg/m3. In view of different notations and limits for BeAir-Conc recommended by various agencies and limited information about Be safety-related details, all relevant information regarding Be safety along with the evolution of Be safety standards over the last 70 years is included in the present article. This is an important issue for the safety of individual's at occupational workplaces as well as for environmental safety and its compilation was highly needed for providing comprehensive information on Be safety from the inception of standards to till today.

Reconciliation of Theoretical Lifetimes of the 5s5p Metastable State for 88Sr with Measurement: The Role of the Blackbody-Radiation-Induced Decay

We conducted measurement and calculation to resolve the long-standing large discrepancy in the metastable state lifetime for the 88Sr atom between theoretical and experimental results. The present lifetime s, measured using the magneto-optical trap as a photon amplifier to detect the weak decay events, is approximately 60% larger than the previous experimental value s. By considering the electron correlation effects in the framework of the multiconfiguration Dirac–Hartree–Fock theory, we obtained a theoretical lifetime of 1079(54) s, which lies in the range of measurements with error bars. Furthermore, we considered the higher-order electron correlation and Breit interaction to control the uncertainty of the theoretical calculation. The significant improvement in the agreement between calculations and measurements is attributed to the updated blackbody radiation-induced decay rate.

Impact of export and import on value addition of ready-made garments sector in Bangladesh

Bangladesh has achieved the prestigious second place among the biggest textile exporters countries. The paper's main objective was to explore the relationship between value addition from ready-made garments with export and import from that sector. The study further focused on the influence of previous exports, imports, and value addition on the current value addition. The study was based on three time series variables, namely export, import, and value addition from ready-made garments of Bangladesh, using data from the Bangladesh Central Bank (Bangladesh Bank) for the years 1994 to 2020. The time series variables exhibited the unit root problem and were converted to stationary after the first difference, checked by Augmented Dickey-Fuller (ADF) Test. The multiple linear regression model illustrated, on the one hand, a highly significant and positive relationship between value addition and export, on the other hand, a highly significant and negative relationship between value addition and import for the ready-made garments in Bangladesh. Moreover, the paper used the Vector Autoregression (VAR) model to explore the influences of lag values of those three variables on value addition. The results suggested that causation among the variables was significant in the short run, but insignificant in the long run. In conclusion, Bangladesh's ready-made garment industry plays an important role in the economy, and it is prudent to expand manufacturing with more value addition in the future.

Immature Platelet Fraction and Its Kinetics in Neonates.

Thrombocytopenia is a common abnormality encountered in the neonatal period, and immature platelet fraction (IPF) may be an informative indicator of thrombopoiesis; however, data on IPF in neonates are scarce. To define reference intervals (RIs) and factors affecting IPF in neonates, we measured the IPF of 533 consecutive neonates. With a multiple regression analysis of 330 newborns with normal platelet counts at birth, premature delivery, neonatal asphyxia, intrauterine infection, chromosomal abnormalities, and respiratory disorders were identified as independent factors for IPF%. The RIs of IPF% and absolute IPF value in neonates were determined to be 1.3% to 5.7% and 3.2 to 14.5×10 9 /L, respectively. On day 14 after birth, IPF% increased to twice the value at birth and thereafter returned to the previous value on day 28. Reticulocyte counts, in contrast, were the lowest at day 14. IPF% was increased in 16 thrombocytopenic patients with various clinical conditions, especially those with immune-mediated thrombocytopenia. IPF in neonates may be evaluated essentially based on the same RIs as in adults, although some precautions must be taken when evaluating IPF in neonates in the first 2 weeks of life. IPF may be useful for evaluating thrombopoiesis and thrombocytopenia in neonates.

The Timing of Potential Last Nucleosynthetic Injections into the Protosolar Molecular Cloud Inferred from 41Ca–26Al Systematics of Bulk CAIs

Short-lived radionuclides (SLRs) provide important information about the chronology of the early solar system. Among them, 41Ca, due to its decay to 41K with a half-life of only 0.1 Ma, is particularly valuable in constraining the timescales and origins of both SLRs and the formation of the oldest solar system materials, the Ca–Al-rich inclusions (CAIs). The initial abundance of 41Ca in the solar system, expressed as the (41Ca/40Ca)I ratio, is the key to unveiling the origin of this nuclide. Here, we report a new solar system (41Ca/40Ca)I ratio of 2.0 × 10−8 derived from the K isotope compositions of two CAIs. This new ratio is about four times higher than the previous value inferred from a mineral isochron. Such a high (41Ca/40Ca)I ratio in the CAIs exceeds that expected for the protosolar molecular cloud by ∼1000×, implying very late injection of the 41Ca (and possibly other SLRs) into the protosolar molecular cloud. The correlated enrichments of 41Ca and 26Al in the bulk CAI samples hint at a common stellar origin of both SLRs. The injection time estimated from our new data depends on the stellar source—it ranges from 0.6 Ma for a Wolf–Rayet wind to 1.0 Ma for a TP-AGB star ejecta.

A continuous intravenous insulin infusion protocol to manage high-dose methylprednisolone-induced hyperglycemia in patients with severe COVID-19

BackgroundMany patients with severe COVID-19 have impaired glucose tolerance, and steroid therapy is a standard treatment. Thus, good glycemic control is important and correlates with better patient outcomes. We began using a continuous intravenous insulin infusion protocol for glycemic control whose infusion rate changes based on the currently measured value and previous value. This study aimed to evaluate this protocol for COVID-19 patients requiring mechanical ventilation.MethodsThis single-center, retrospective, case control study was conducted on all adult patients who required mechanical ventilation for severe COVID-19 pneumonia admitted to our critical care center from April 1, 2020 through June 20, 2021. Blood glucose levels were measured in all patients every 4 h after admission. We started using the insulin infusion protocol from August 1, 2020. Patients before starting the protocol comprised the non-protocol group and those after starting the protocol comprised the protocol group. Blood glucose levels and hypo- or hyperglycemia events were compared between groups. We also surveyed ICU nurses about their experience using the protocol.ResultsDuring the study period, 173 patients with COVID-19 were admitted. After 15 patients were excluded for several reasons, the study included 158 patients: non-protocol group (n = 14) and protocol group (n = 144). In the initial phase (days 1–2), blood glucose levels of the protocol group were higher compared with the non-protocol group, and as the number of measurements increased, blood glucose levels were gradually brought under control within the target range in the protocol group. Almost no hypoglycemic events (blood glucose < 80 mg/dL) were detected in either group. The rate of hyperglycemia (blood glucose > 300 mg/dL) was about 5–10% in the initial phase in the protocol group and about 10–15% in the early phase (days 3–4) in the non-protocol group. The questionnaire survey revealed that 80% of ICU nurses responded favorably.ConclusionsThis insulin protocol gradually brought the blood glucose level within target levels in severe COVID-19 patients treated with high-dose steroid. Some hyperglycemia events were detected despite patients being under the protocol in the initial phase, and thus, minor modifications of the protocol might be required in the initial phase.

Peranan Badan Usaha Milik Desa (BUMDes) dalam Perekonomian Masyarakat Desa Kabupaten Aceh Besar

BUMDes is a village business formed/established by the village government where capital ownership and management are carried out by the village government and the community. The purpose of the establishment of BUMDes is the government's effort to improve the financial capacity of the village government in administering government and increasing community income through various economic business activities of rural communities. The purpose of this study was to find out how the role of Village Owned Enterprises (BUMDes) in the economy of the community in Aceh Besar District. The locations used as research sites are Blang Krueng Village, Lambro Deyah Village and Meunasah Krueng Village, Aceh Besar District. The location selection is done by (purposive sampling). Purposive Sampling is the determination of the sample with certain considerations such as considering that the respondent is involved in BUMDes activities. The analytical method used in this study is a quantitative descriptive method. The results of this study can be concluded that the existence of BUMDes has an influence on rural communities, where the previous mean income value is 1.85, namely income category 1 which is 0 – 1,000,000 and category 2 is 1,000,000 – 2,000,000. The mean value of the respondent's income after joining has changed to 2.78, which is in category 3 which is 2,000,000 – 3,000,000 and category 4 is 3,000,000. The results of this study can be concluded that the existence of BUMDes has an influence on rural communities, where the previous mean income value is 1.85, namely income category 1 which is 0 – 1,000,000 and category 2 is 1,000,000 – 2,000,000. The mean value of the respondent's income after joining has changed to 2.78, which is in category 3 which is 2,000,000 – 3,000,000 and category 4 is 3,000,000. The results of this study can be concluded that the existence of BUMDes has an influence on rural communities, where the previous mean income value is 1.85, namely income category 1 which is 0 – 1,000,000 and category 2 is 1,000,000 – 2,000,000. The mean value of the respondent's income after joining has changed to 2.78, which is in category 3 which is 2,000,000 – 3,000,000 and category 4 is 3,000,000.Income leveltan community economyt the village before and after the existence of BUMDes there was a displacement, meaning that bumdes plays a role inincome transfertan community economyt. Before joining BUMDes, the income was on the low criteria as many as 30 people, after joining 4 people. On the criteria of moderate income before joining as many as 18 people after joining to 25 people. On the high income criteria before joining there were 14 people after joining 17 people. On very high income criteria before joining 3 people after joining 17 people. Keywords: BUMDes, Economy, Income.

VLSI Architecture of Decision Based Adaptive Denoising Filter for Removing Salt &amp; Pepper Noise

A new Decision Based Adaptive Denoising Filter (DBADF) algorithm &amp; hardware architecture are proposed for restoring the digital image that is highly corrupted with impulse noise. The proposed DBADF detects only the corrupted pixels and that pixel is restored by the noise-free median value or previous value based upon the noise density in the image. The proposed DBADF uses a window initially and adaptively goes up to window based on the noise corruption more than 50% by impulse noise in the current processing window. The proposed architecture was found to exhibit better visual qualitative and quantitative evaluation based on PSNR, IEF, EKI, SSIM, FOM, and error rate. The DBAMF architecture also preserves the original information of digital image with a high density of salt &amp; pepper noise, when compared to many standard conventional algorithms. The proposed architecture has been simulated using the VIRTEX7 FPGA device and the reported maximum post place and route frequency are 149.995MHz and the dynamic power consumption is 179mW.

A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction

Nowadays, air pollution is an important problem with negative impacts on human health and on the environment. The air pollution forecast can provide important information to all affected sides, and allows appropriate measures to be taken. In order to address the problems of filling in the missing values in the time series used for air pollution forecasts, the automation of the allocation of optimal subset of input variables, the dependency of the air quality at a particular location on the conditions of the surrounding environment, as well as automation of the model’s optimization, this paper proposes a deep spatiotemporal model based on a 2D convolutional neural network and a long short-term memory network for predicting air pollution. The model utilizes the automatic selection of input variables and the optimization of hyperparameters by a genetic algorithm. A hybrid strategy for missing value imputation is used based on a combination of linear interpolation and a strategy of using the average between the previous value and the average value for the same time in other years. In order to determine the best architecture of the spatiotemporal model, the architecture hyperparameters are optimized by a genetic algorithm with a modified crossover operator for solutions with variable lengths. Additionally, the trained models are included in various ensembles in order to further improve the prediction performance—these include ensembles of models with the same architecture comprising the best architecture obtained by the evolutionary optimization, and ensembles of diverse models comprising the k best models of the evolutionary optimization. The experimental results for the Beijing Multi-Site Air-Quality Data Set show that the proposed spatiotemporal model for air pollution forecasting provides good and consistent prediction results. The comparison of the suggested model with other deep NN models shows satisfactory results, with the best performance according to MAE, based on the experimental results for the station at Wanliu (16.753 ± 0.384). Most of the model architectures obtained by the optimization of the model hyperparameters using the genetic algorithm have one convolutional layer with a small number of kernels and a small kernel size; the convolutional layers are followed by a max-pooling layer, and one or two LSTM layers are utilized with dropout regularization applied to the LSTM layer using small values of p (0.1, 0.2 and 0.3). The utilization of ensembles from the k best trained models further improves the prediction results and surpasses other deep learning models, according to MAE and RMSE metrics. The used hybrid strategy for missing value imputation enhances the results, especially for data with clear seasonality, and produces better MAE compared to the strategy using average values for the same hour of the same day and month in other years. The experimental results also reveal that random searching is a simple and effective strategy for selecting the input variables. Furthermore, the inclusion of spatial information in the model’s input data, based on the local neighborhood data, significantly improves the predictive results obtained with the model. The results obtained demonstrate the benefits of including spatial information from as many surrounding stations as possible, as well as using as much historical information as possible.

Monitoring network design with MLPG-TLBO hybrid model (case study Birjand, Iran)

As the groundwater quantitative monitoring aimed to determine the factors affecting the aquifer behavior plays an important role in its regional management, studying the temporal and spatial groundwater level variations requires a comprehensive monitoring network. Effort has been made in this study to introduce a new linked simulation–optimization method, named MLPG-TLBO to quantitatively monitor the Birjand aquifer and determine the optimal points for piezometers. This model uses meshless local Petrov Galerkin (MLPF) method in the simulation part and teaching–learning-based optimization (TLBO) method in the optimization part. The objective function, in this study, is to minimize the difference between the groundwater level observed in piezometers and obtained computationally by the model. Since this coupled model is independent from the meshing, it eliminates the mesh-dependent shortcomings and, hence, yields more accurate results. It has been calibrated and validated in previous Birjand area studies and has led to acceptable results. By implementing the model in Birjand aquifer, the optimal positions of ten piezometers were determined mostly in areas where the density of the extraction wells was lower. Finally, the RMSE of both MLPG-TLBO and FDM was obtained to be 0.334 m and 1.483 m for 10 optimal piezometers. The RMSE value for MLPG-TLBO has shown a 0.423 m reduction compared to its previous value. This difference is quite meaningful as it shows good performance of this method in designing an optimal network for the aquifer.

Intrinsic Mobility of Low-Density Electrons in Photoexcited Diamond

The cyclotron-resonance method reveals the drift mobility of carriers in semiconductors, which determines a device's (opto)electronic functionality. However, determining the intrinsic mobility value without interference from other carriers, dislocations, impurities, etc. remains challenging. By minimizing the density of photoexcited carriers in ultrapure diamond, the authors find an extraordinarily narrow cyclotron-resonance curve for electrons in diamond at 3 K. In this manner they obtain a corrected mobility value of 10${}^{8}$ cm${}^{2}$ V${}^{\ensuremath{-}1}$ s${}^{\ensuremath{-}1}$, a 16-fold increase compared to the previous record value for diamond.

Robot navigation in a crowd by integrating deep reinforcement learning and online planning

Navigating mobile robots along time-efficient and collision-free paths in crowds is still an open and challenging problem. The key is to build a profound understanding of the crowd for mobile robots, which is the basis of a proactive and foresighted policy. However, since the interaction mechanisms among pedestrians are complex and sophisticated, it is difficult to describe and model them accurately. For the excellent approximation capability of deep neural networks, deep reinforcement learning is a promising solution to the problem. However, current model-free learning-based approaches in crowd navigation always neglect planning and still lead to reactive collision avoidance policies and shortsighted behaviors. Meanwhile, most model-based learning-based approaches are based on state values, imposing a substantial computational burden. To address these problems, we propose a graph-based deep reinforcement learning method, social graph-based double dueling deep Q-network (SG-D3QN), that (i) introduces a social attention mechanism to extract an efficient graph representation for the crowd-robot state, (ii) extends the previous state value approximator to a state-action value approximator, (iii) further optimizes the state-action value approximator with simulated experiences generated by the learned environment model, and (iv) then proposes a human-like decision-making process by integrating model-free reinforcement learning and online planning. Experimental results indicate that our approach helps the robot understand the crowd and achieves a high success rate of more than 0.99 in the crowd navigation task. Compared with previous state-of-the-art algorithms, our approach achieves better performance. Furthermore, with the human-like decision-making process, our approach incurs less than half of the computational cost.

Highly Efficient Solar Laser Pumping Using a Solar Concentrator Combining a Fresnel Lens and Modified Parabolic Mirror

Solar-pumped lasers (SPLs) allow direct solar-to-laser power conversion, and hence, provide an opportunity to harness a renewable energy source. Herein, we report significant improvements in end-side-pumped solar laser collection efficiency and beam brightness using a novel 1.5-m-diameter compound solar concentrator combining a Fresnel lens and modified parabolic mirror. A key component of this scheme is the off-axis-focused parabolic mirror. An original dual-parabolic pump cavity is another feature. To determine the dependence of the SPL performance on the distance between the focus and central axis of the modified parabolic mirror, several systems with different distances were optimized using TracePro and ASLD software. It was numerically calculated that end-side pumping a 5-mm-diameter, 22-mm-long Nd:YAG crystal rod would generate 74.6 W of continuous-wave solar laser power at a collection efficiency of 42.2 W/m2, i.e., 1.1 times greater than the previous record value. Considering the laser beam quality, a brightness figure of 0.063 W was obtained, which is higher than that of other multimode SPL designs with end-side pumping. Thus, our SPL concentrator offers the possibility of achieving a beam quality as high as that obtainable via side pumping, alongside highly efficient energy conversion, which is characteristic to end-side pumping.