Miracle-dz/UniFAR-FTUs huggingface dataset:
A value-sensitive design approach to minimize value tensions in software-based risk-assessment instruments

ABSTRACT Many authorities currently use software-based risk-assessment instruments (SBRAIs) to assess a person’s potential to commit crimes (e.g. terrorism, sexual assault). SBRAIs promise better, faster, and more consistent decision-making than their paper-based predecessors. The underlying SBRAI classification schema is well-understood, but given the potentially serious ethical consequences of misjudgments, there is a lack of understanding of how a newly introduced IT artefact, or digital transformation, affects the overall process. Applying a value-centric lens and using Value Sensitive Design (VSD), we identify users’ most relevant SBRAI values, as well as the existing value tensions between developers and users. We find that various value tensions, which had not been an issue prior to digitization, occur. These are tensions regarding the values traceability, reliability, neutrality, support, and trust. These value tensions often emerge due to misunderstandings and miscommunications that unclear classifications enable. Practitioners should therefore focus on four design guidelines to avoid SBRAI value tensions: constant and accessible information, transparency, isolated use anticipation, and compliance with novice and expert users’ requirements.
Genotoxic and Mutagenic Effects of the Antifouling Biocide Chlorothalonil on the Estuarine Fish Micropogonias Furnieri, Desmarest, 1823

Chlorothalonil is a widely used fungicide in agriculture, and as biocide in antifouling paints. Although it causes toxic effects on non-target organisms and can bioaccumulate in fish tissues, little is known about its sublethal effects. Thus, we evaluated the genotoxic and mutagenic effects of chlorothalonil in Micropogonias furnieri, an estuarine and commercially important fish with potential as a test organism for ecotoxicology assays. We showed that chlorothalonil exerts genotoxic (DNA damage) and mutagenic (micronuclei and nuclear abnormalities) in a dose-dependent manner (0.35 and 3.5 μg g-1). As the genomic instability may lead to carcinogenesis, our data can assist decision-makers with evidence for banning this compound since any benefit to portuary activities and maritime navigation is outweighed by the cost to aquatic ecosystems and to human health.
Spatial and temporal ways of knowing sea level rise: Bringing together multiple perspectives

Sea level rise presents risks to ecosystems, populations, and infrastructure in low‐lying areas. This article considers diverse ways of knowing, understanding, and experiencing these risks. It explores differences and connections between knowledge produced through the technological methods of scientific research and that which emerges through the experiences and insights of local people. For example, while scientific assessments measure and forecast, among other things, the height and rate of vertical change in the sea level using instruments such as tide gauges and radar‐firing satellites, for local populations sea level rise is largely perceived and knowable through everyday processes and lived experiences of coastal changes as sea waters encroach onto the land. The review article reveals this diversity of knowledge and how it is produced. It considers how these different forms of knowledge might coalesce in ways that can more effectively inform understandings of, and responses to, the varied effects of sea level rise. Focusing specifically on spatial and temporal understandings of sea level rise—as the vertical rise of sea levels and inward encroachment of sea water, and timescales from the everyday to the decadal and centennial—this article concludes by arguing for the importance of integrating scientific measurement and modeling with local knowledge. It suggests that local and Indigenous knowledge should not merely represent an enrichment of scientific facts, but rather that bringing together local/Indigenous and scientific knowledge can provide significant ways of knowing and sensing the world that can build the resilience of social‐ecological systems.
Dynamic Performance Analysis of VSC-HVDC Based Modular Multilevel Converter under Fault

The use of high voltage direct current based voltage source converter (VSC-HVDC) in power transmission systems knows a great progress in recent years. Above all, with the new generation of power electronics converters such as the modular multi-level converter (MMC), with his scalable structure it can theoretically meet any voltage level requirement, which allows to increase the size of the power transferred compared to conventional converters. In this sense, this paper presents a study of a VSC-HVDC system based on a modular multi-level converter (MMC). The main objective of this work is to analyze the performance of the VSC-HVDC system based MMC without the AC filters and its control in the event of a fault, during set point changes and unbalanced grid conditions. After realization a mathematical model of the system studied and its control, simulations are done over in Simpower System/Matlab. The results obtained confirm the robustness of the system control and the system gives a good energy quality, that manifests by a good output currant and voltage curves with no need to use a voluminous AC filter.
Glutathione Levels and Glutathione-Glutamate Correlation in Patients With Treatment-Resistant Schizophrenia

Abstract Treatment-resistant schizophrenia (TRS) has been suggested to involve glutamatergic dysfunction. Glutathione (GSH), a dominant antioxidant, is known to be involved in glutamatergic neurotransmission. To date, no study has examined GSH levels in patients with TRS. The aim of this study was to examine GSH levels in the dorsal anterior cingulate cortex (dACC) of patients with TRS. Patients with schizophrenia were categorized into 3 groups with respect to their antipsychotic response: (1) clozapine (CLZ) nonresponders, (2) CLZ responders, and (3) first-line responders (FLR). GSH and glutamine + glutamate (Glx) levels were measured using 3T proton magnetic resonance spectroscopy. Firstly, dACC GSH levels were compared among the patient groups and healthy controls (HCs). Further, relationships between GSH and Glx levels were compared between the groups and GSH levels were explored stratifying the patient groups based on the glutamate-cysteine ligase catalytic (GCLC) subunit polymorphism. There was no difference in GSH levels between the groups. FLR showed a more negative relationship between GSH and Glx levels in the dACC compared to HCs. There were no effects of GCLC genotype on the GSH levels. However, CLZ responders had a higher ratio of high-risk GCLC genotype compared to CLZ nonresponders. This study demonstrated different relationships between GSH and Glx in the dACC between groups. In addition, the results suggest a potential link between CLZ response and GCLC genotype. However, it still remains unclear how these differences are related to the underlying pathophysiology of schizophrenia subtypes or the mechanisms of action of CLZ.
Zeolites Enhance Soil Health, Crop Productivity and Environmental Safety

In modern days, rapid urbanisation, climatic abnormalities, water scarcity and quality degradation vis-à-vis the increasing demand for food to feed the growing population necessitate a more efficient agriculture production system. In this context, farming with zeolites, hydrated naturally occurring aluminosilicates found in sedimentary rocks, which are ubiquitous and environment friendly, has attracted attention in the recent past owing to multidisciplinary benefits accrued from them in agricultural activities. The use of these minerals as soil ameliorants facilitates the improvement of soil’s physical and chemical properties as well as alleviates heavy metal toxicity. Additionally, natural and surface-modified zeolites have selectivity for major essential nutrients, including ammonium (NH4+), phosphate (PO42−), nitrate (NO3−), potassium (K+) and sulphate (SO42−), in their unique porous structure that reduces nutrient leaching. The slow-release nature of zeolites is also beneficial to avail nutrients optimally throughout crop growth. These unique characteristics of zeolites improve the fertilizer and water use efficiency and, subsequently, diminish environmental pollution by reducing nitrate leaching and the emissions of nitrous oxides and ammonia. The aforesaid characteristics significantly improve the growth, productivity and quality of versatile crops, along with maximising resource use efficiency. This literature review highlights the findings of previous studies as well as the prospects of zeolite application for achieving sustenance in agriculture without negotiating the output.
Supplementary material to "An Alpine Drought Impact Inventory to explore past droughts in a
mountain region"

Abstract. Drought affects even mountain regions, despite a humid climate. Droughts' damaging character in the past and an increasing probability in future projections call for an understanding of drought impacts in the European Alpine region. The European Drought Impact Report Inventory (EDII) collects text reports on negative drought impacts. This study presents a considerably updated EDII focusing on the study region of the greater Alpine Space. This first version release of an Alpine Drought Impact Inventory (EDIIALPS) classifies impact reports into categories covering various affected sectors and enables comparisons of the drought impact characteristics. We analyzed the distribution of reported impacts on the spatial, temporal and seasonal scale, and by drought type for soil-moisture and hydrological drought. For the spatial analysis, we compared the impact data located in the Alpine Space' to entire Europe. Further, we compared impact data between different climatic and altitudinal domains (Northern vs. Southern region, pre-Alpine vs. high-altitude region), and between the Alpine countries. Compared to entire Europe, in the Alpine Space agriculture and livestock farming impacts are even more frequently reported, especially in the Southern region. Public water supply is second most relevant sector, but overall less prominent compared to Europe, especially in spring when snowmelt mitigates water shortages. Impacts occurred mostly in summer and early autumn with a delay between those impacts initiated by soil-moisture and those by hydrological drought. The high-altitude region showed this effect the strongest. From 1975 to 2020, the number of archived reports increased, with substantially more impacts noted during the drought events of 1976, 2003, 2015 and 2018. Moreover, reported impacts diversified from agricultural dominance to multi-facetted impact types covering forestry, water quality, industry and so forth. Though EDIIALPS is biased by reporting behaviour, the amount of more than 3200 compiled reports on negative drought impacts demonstrates the need to move from emergency actions to better preparedness. These may be guided by EDIIALPS' insights to regional patterns, seasons and drought types.
On-Line Optimization of Microgrid Operating Cost Based on Deep Reinforcement Learning

In view of the microgrid random optimization scheduling problem, this paper proposes a microgrid online optimization algorithm based on deep reinforcement learning. By using the approximate state-action value function of the deep neural network, the action of the battery is discretized into the decision variables output by the neural network, and then the remaining decision variables are solved by nonlinear programming and the immediate return is calculated. The optimal strategy is obtained by using the Q-learning algorithm. In order to make the neural network adapt to the randomness of wind, photovoltaic and load power, according to the wind, photovoltaic and load power prediction curves as well as wind, photovoltaic and load prediction errors, Monte Carlo sampling was used to generate multiple sets of training curves to train the neural network. After the training is completed, the weight is fixed and the real-time action value of the battery is output according to the real-time status of the microgrid, so as to realize the online optimal dispatching of the microgrid. Compared with day-ahead optimization results under different fluctuations of wind, photovoltaic and load power, the effectiveness and superiority of this algorithm in online optimization of microgrid are verified.
Low-Pass Filter Based Integrated 5G Smartphone Antenna for Sub-6-GHz and mm-Wave Bands

To maintain the compactness of future 5G smartphones, integration of sub-6 GHz and millimeter-wave (mm-wave) bands is a challenging task due to the large frequency ratio. We, therefore, propose a single-fed triple-band antenna with a large frequency ratio. The proposed antenna is composed of a microstrip patch radiator with an inverted U-shaped slot for dual-band operation in the mm-wave frequencies (28 and 38 GHz). A meandered radiating structure is linked to the microstrip patch radiator through a compact microstrip resonant cell (CMRC) low-pass filter (LPF), which is used for the integration of these structures to be operated at 3.5 GHz. A compact size of 22 mm $\times10$ mm $\times0.508$ mm for the proposed antenna was realized by incorporating the radiator slot, an LPF, the truncated ground, and a meandered line structure. Wide, decoupled −10 dB bandwidths of 12.9%, 5.8%, and 2.4% at 3.5, 28, and 38 GHz, respectively, were realized for the proposed MIMO antenna without utilizing an external decoupling structure. A detailed safety study with respect to specific absorption rates and power densities was analyzed in data, reading, and talking modes. Finally, the measurements were performed in an anechoic chamber, and the measured results were in a good correlation with the simulation results.
Effect of Cold Water Intake During Exercise in Heat on Cognitive Function

Regular exercise is recommended for most people to improve health, but participation to exercise is often hampered by environmental conditions [1]. In particular, exercise in hot environments increases the skin blood flow and sweating rate for regulation of body temperature leading to a body water depletion and an imbalance of body homeostasis [2]. The increased depletion of body water by sweating also decreases venous return inducing a cardiac performance drift limiting exercise capacities [1,3]. The elevation of body temperature and dehydration caused by heat exposure affect the normal body functioning and mood of the brain, which can adversely affect cognitive function [4,5]. Cognitive function is considered a very important factor during exercises and performing sports which require physical skills and mental judgment. Cognitive function during exercise is known to be affected by dehydration, body temperature elevation, and hypoglycemia [6]. When the body becomes dehydrated for a long time or acutely, the blood flow to the cerebrovascular vessels decreases, and changes in electrolytes such as hypernatremia and hormonal changes such as glucocorticoid and vasopressin can negatively affect the physical function [7]. Cold treatments against body temperature elevation and dehydration is one of the strategy to prevent heat injuries and physical performance declines. Cold treatment refers to a method of cooling body such as drinking cold water, exposure to cold air, and/or wearing an ice vest beORIGINAL ARTICLE ISSN(Print) : 1226-1726 ISSN(Online) 2384-0544
Hyperparameter experiments on end-to-end automatic speech
 recognition*

End-to-end (E2E) automatic speech recognition (ASR) has achieved promising performance gains with the introduced self-attention network, Transformer. However, due to training time and the number of hyperparameters, finding the optimal hyperparameter set is computationally expensive. This paper investigates the impact of hyperparameters in the Transformer network to answer two questions: which hyperparameter plays a critical role in the task performance and training speed. The Transformer network for training has two encoder and decoder networks combined with Connectionist Temporal Classification (CTC). We have trained the model with Wall Street Journal (WSJ) SI-284 and tested on devl93 and eval92. Seventeen hyperparameters were selected from the ESPnet training configuration, and varying ranges of values were used for experiments. The result shows that “num blocks” and “linear units” hyperparameters in the encoder and decoder networks reduce Word Error Rate (WER) significantly. However, performance gain is more prominent when they are altered in the encoder network. Training duration also linearly increased as “num blocks” and “linear units” hyperparameters’ values grow. Based on the experimental results, we collected the optimal values from each hyperparameter and reduced the WER up to 2.9/1.9 from dev93 and eval93 respectively. and 2.6/2.5 respectively, but 3.4/3.5, and 0.8/0.6 in the decoder network. A “dropout rate” hyperparameter in the decoder network does not act like the one in the encoder network, but it reaches the lowest WER at the value 0.1 and maintains high WER at the other values. Meaningful result is not found in “attention heads” and “self attention dropout rate”.
High order plasmonic vortex generation based on spiral nanoslits

Highly localized plasmonic vortices carrying orbital angular momentum are of importance for many applications. Yet, it is a challenge to generating plasmonic vortex with a high topological charge because of no available technique. Here, a novel plasmonic vortex generator is proposed based on spiral nanoslits etched in a metal film, which can produce a high order plasmonic vortex. The consecutive spiral nanoslit can generate plasmonic vortex with high intensity and the segmented nanoslits enhances the controllability of the plasmonic vortex, which are demonstrated numerically and experimentally. High order plasmonic vortex generation will broaden the prospects for plasmonic vortices in practical applications for nanomanipulation and nanofabrication.
Dung beetle activity had no positive effect on nutrient concentration or performance of established rainforest seedlings

Dung beetle activity causes many changes in the soil when they remove feces from the surface. In temperate grasslands and in greenhouse experiments, these changes have been found to positively affect established plants, but information about these effects under natural conditions in tropical forests is practically nonexistent. In a tropical rainforest (Los Tuxtlas, Mexico), we carried out field experiments with seedlings of six tree species (Brosimum alicastrum, Calophyllum brasiliense, Cymbopetalum baillonii, Diospyros digyna, Omphalea oleifera, and Poulsenia armata), to assess whether feces burial by dung beetles had any positive effect on them. We estimated foliar nitrogen and phosphorus, seedling survival, growth and resource allocation, under three treatment levels: (a) feces added and beetles active, (b) feces added and beetles excluded, and (c) no feces added (and consequently no beetles active). We analyzed data at two levels: community (all plant species together) and individual species. Dung beetle activity did not increase the concentration of foliar nutrients or seedling performance (survival, growth); in one species, it modified resource allocation. At the community level and for two species, dung beetle activity decreased the probability of seedlings growing in height. Our study provides evidence on how extrapolating the effects of dung beetle activity from other study systems into tropical forests can be problematic. More studies will be needed before we can have a clearer idea of the effects that feces burial by dung beetles have on established plants in tropical forests.
Human daily activities recognition using decision tree

Decision tree is a supervised classifier that is easy to understand. There are various decision tree methods. This study aimed to compare the performance of decision tree methods in human activity recognition using acceleration and jerk data. The subjects performed human activity daily living, namely walking on a flat surface, walking upstairs, walking downstairs, sitting, standing, and lying down. The features were grouped into three categories: acceleration features, jerk features, and combined features of acceleration and jerk. The evaluation was done using Random Forest, J48, Logistic Model Tree, Reduced Error Pruning Tree, Decision Stump, Random Tree, and Hoeffding Tree. The results showed that Random Forest outperformed the other classifiers with acceleration features performed better than the jerk features. However, the combined acceleration and jerk features yielded the highest accuracy. In conclusion, Random Forest is the best decision tree technique in recognizing the pattern in human activity.
Econometric models of duration data in entrepreneurship with an application to start-ups' time-to-funding by venture capitalists (VCs)

Because time is a key determinant of entrepreneurial decision making, time-to-event models are ubiquitous in entrepreneurship. Widespread econometric misconception, however, may cause complicated biases in existing studies. The reason is spurious duration dependency, a complicated form of endogeneity caused by unobserved heterogeneity, which is particularly pronounced in entrepreneurship data. This article discusses the endogeneity problem and methods to ‘debias’ time-to-event models in entrepreneurship. Simulations and empirical evidence indicate that only the frailty approach yields consistently unbiased parameter estimates. An application to start-up firms' time-to-funding shows that other methods lead to dramatic biases. Therefore, this article advocates a paradigm shift in the modeling of time variables in entrepreneurship.
Investigation of Weighted Scales for Measuring Visual Fatigue in Screening Tasks

The "screening" trend of modern society has been a progressively increasing burden on the human visual system, and visual fatigue problems are attracting growing attention. Nowadays, subjective testing is the most widely used measure for visual fatigue; however, the low accuracy of subjective testing has been hindering its further improvement. Motivated by the idea of weighted scoring, this study investigated the effects of two weighted scales for measuring visual fatigue in screening tasks. Specifically, a questionnaire with 10 items collected from the classic scales was performed with eye-tracking testing in two typical screen visual fatigue experiments, i.e., searching and watching. Then the subjective scores were factor-analyzed into three subscales before attempting linear regression analyses, which set the dependents to two previously validated eye-tracking parameters, i.e., fixation frequency and saccade amplitude. Finally, two weighted scales were obtained in assessing visual fatigue of varying levels, which demonstrated the potential to improve testing accuracy of visual fatigue with the calibration of objective measurement.