Product Metrics Research Articles

Cryptographic Engineering a Fast and Efficient SIKE in FPGA

Recent attacks have shown that SIKE is not secure and should not be used in its current state. However, this work was completed before these attacks were discovered and might be beneficial to other cryptosystems such as SQISign. The primary downside of SIKE is its performance. However, this work achieves new SIKE speed records even using less resources than the state-of-the-art. Our approach entails designing and optimizing a new field multiplier, SIKE-optimized Keccak unit, and high-level controller. On a Xilinx Virtex-7 FPGA, this architecture performs the NIST Level 1 SIKE scheme key encapsulation and key decapsulation functions in 2.23 and 2.39 ms, respectively. The combined key encapsulation and decapsulation time is 4.62 ms, which outperforms the next best Virtex-7 implementation by nearly 2 ms. Our implementation achieves speed records for the NIST Level 1, 2, and 3 parameter sets. Only our NIST Level 5 parameter set was beat by an all-out performance implementation. Our implementations also efficiently utilize the FPGA resources, achieving new records in area-time product metrics for all parameter sets. Overall, this work continues to push the bar for accelerating SIKE computations to make a stronger case for SIKE standardization.

Defining And Measuring Variance in Clinical Productivity Metrics By Training Level

Defining And Measuring Variance in Clinical Productivity Metrics By Training Level

A broader view of research contributions: Necessary adjustments to DORA for hiring and promotion in psychology.

Recently Schönbrodt et al. (2022) released recommendations for improving how psychologists could be evaluated for recruitment, retention, and promotion. Specifically, they provided four principles of responsible research assessment in response to current methods that rely heavily on bibliometric indices of journal quality and research impact. They build their case for these principles on the San Francisco Declaration on Research Assessment (DORA) perspective that decries reliance on invalid quantitative metrics of research quality and productivity in hiring and promotion. The paper makes clear the tension panels have to address in evaluating applications—too little time to do an in-depth evaluation of an individual’s career and contribution, so reliance on easy to understand, but perhaps invalid, metrics. This dilemma requires an alternative mechanism rather than simply a rejection of metrics. To that end, the authors are to be congratulated for operationalising what those alternatives might look like. Nonetheless, the details embedded in the principles seem overly narrow and restrictive.

Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes

Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variation in pest ecology, but also from the widespread use of categorical land-use maps that do not adequately characterize habitat-availability for pests. We used 1163 field-year observations of Lygus hesperus (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely-sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because L. hesperus often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that L. hesperus densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.

Salt type, sugar addition, and system type in intensive RAS for Pacific white shrimp (Litopenaeus vannamei) production

The use of a low-cost, homemade salt mixture in hybrid RAS may reduce operational costs for inland marine aquaculture producers. The addition of sugar in RAS may benefit shrimp growth by making suspended microbes richer in protein, which shrimp can then consume. Increased digestive enzyme activity has been noted in shrimp raised in systems with a substantial abundance of biofloc particles. However, there is still debate over whether clear-water RAS may be superior to particulate-rich systems. This study examined the effects of system type, low-cost salt, and sugar addition on the production, water quality, and digestive enzyme activity of L. vannamei. In the study, two levels of each experimental factor were used. The factors used were system type (clear water-CW vs hybrid-HY), salt type (Least Cost-L vs Commercial-C), and sugar (presence or absence of sugar addition: S vs. N). There was a total of eight treatments: HY-L-S, HY-L-N, HY-C-S, HY-C-N, CW-L-S, CW-L-N, CW-C-S, and CW-C-N, each of which had three randomly assigned, replicate, 1 m3 tanks. All tanks were stocked with 250 shrimp per m3 (3.1 g average weight), extra filtration was used in CW tanks, and sugar (sucrose) was added at a targeted C:N ratio of 14:1 in the sugar tanks. At the end of the experiment, the hepatopancreas was removed from shrimp for digestive enzyme analyses. Results from the experiment showed no significant differences in the average weight, total harvest, FCR, and growth rate between system types and salt types. However, the use of sugar resulted in significantly reduced mean individual weight of shrimp. Nitrate concentration was significantly lower and solids concentrations were higher in the tanks receiving sugar compared to those with no sugar addition. There were no significant differences in amylase activity between treatments, but trypsin activity was higher in the HY systems than the CW systems. The results of this study indicate that this particular low-cost salt mixture had no adverse effects on the water quality, production, or enzyme metrics tested. Biofloc particles seem to have enhanced trypsin activity in the HY system shrimp. Lastly, the addition of sugar had some substantial impacts on solids and nitrate concentrations, as well as negative effects on shrimp growth. Both independently and in combination, the factors examined in this project may have important implications for the management of intensive shrimp aquaculture operations.

Curvature Blow-up in Doubly-warped Product Metrics Evolving by Ricci Flow

For any manifold N p N^p admitting an Einstein metric with positive Einstein constant, we study the behavior of the Ricci flow on high-dimensional products M = N p × S q + 1 M = N^p \times S^{q+1} with doubly warped product metrics. In particular, we provide a rigorous construction of local, type II, conical singularity formation on such spaces. It is shown that for any k > 1 k > 1 there exists a solution with curvature blow-up rate ‖ R m ‖ ∞ ( t ) ≳ ( T − t ) − k \| Rm \|_{\infty } (t) \gtrsim (T-t)^{-k} with singularity modeled on a Ricci-flat cone at parabolic scales.

Water productivity is in the eye of the beholder: benchmarking the multiple values produced by water use in the Phoenix metropolitan area

Abstract. Water productivity (or efficiency) data inform water policy, zoning, and planning, along with water allocation decisions under water scarcity pressure. This paper demonstrates that different water productivity metrics lead to different conclusions about who is using water more effectively. In addition to supporting the population's drinking and sanitation needs, water generates many other public and private social, environmental, and economic values. For the group of municipalities comprising the Phoenix metropolitan area, we compare several water productivity metrics by calculating the water value intensity (WVI) of potable water delivered by the municipality to its residential and non-residential customers. Core cities with more industrial water uses are less productive by the conventional efficiency measure of water used per capita, but core cities generate more tax revenues, business revenues, and payroll per unit of water delivered, achieving a higher water productivity by these measures. We argue that policymakers should consider a more diverse set of socio-economic water productivity measures to ensure that a broader set of values are represented in water allocation policies.

Machine learning in employee performance evaluation: A HRM perspective

This research explores how machine learning, the tech whiz kid, is shaking up the traditional HR world of employee reviews. Companies are hungry for better ways to assess their workforce, and machine learning comes to the table with a buffet of data analysis tools. Productivity metrics, project wins, even that qualitative feedback, all get crunched by these algorithms to dish up comprehensive and objective performance pictures. This research dives into both the pros and cons of bringing this tech titan into HR, aiming to make evaluations not just accurate, but also fair and impactful. It's about finding the sweet spot where technology and human understanding join forces to power up performance reviews for the modern workplace. In the ever-evolving dance of the modern workplace, where performance reigns supreme, the old waltz of subjective evaluations stumbles toward obsolescence. This study steps into the spotlight, exploring the transformative potential of machine learning as the new lead partner in HRM, specifically for crafting nuanced and objective performance assessments. Imagine algorithms like virtuoso musicians, weaving together diverse data melodies – productivity's staccato riffs, project outcomes' triumphant crescendos, even the subtle whispers of qualitative feedback – to paint a vibrant portrait of individual contributions. But this data-driven tango isn't without its tricky steps. This research scrutinizes both the grace and potential missteps of machine learning in HRM, aiming to illuminate a path toward optimized performance evaluations that are not only accurate but also fair and effective. By bridging the gap between technology and human understanding, this study offers a roadmap for organizations waltzing towards a future where performance appraisals unlock the full potential of their workforce.

Probabilistic forecasting of construction labor productivity metrics

This study investigates the possibility of doing probabilistic forecasting of construction labor productivity metrics for both long-term and short-term estimates. The research aims to evaluate autoregressive forecasting models, which may help decision-makers with information currently unavailable in construction projects. Unlike point forecasts, the proposed method employs probabilistic forecasting, offering additional valuable insights for decision-makers. The distributional information is obtained by updating the moments of the distribution during training. Two datasets are used to evaluate the models: one collected from an entire construction site for long-term forecasting and one from an individual worker for short-term forecasting. The models aim to predict the state of direct work, indirect work, and waste. Several models are trained using different hyperparameters. The models are tuned on the number of trees and the regularization used. The presented method gives estimates of future levels of direct work, indirect work, and waste, which will add value to future processes.

Individual Radiologist Productivity Metrics: Point-Transparency Among Group Members Is Necessary to Optimize Long-Term Practice Success.

Individual Radiologist Productivity Metrics: Point-Transparency Among Group Members Is Necessary to Optimize Long-Term Practice Success.

Milk Production, Quality Parameters, and Bacterial Colony Counts of Raw Milk from Murrah Buffaloes Reared in Mixed Crop-Livestock Systems

This research aimed to assess the quantity and quality of raw Murrah buffalo milk in a mixed crop-livestock farming system in North Sumatra, Indonesia. The study was conducted at the Sumber Ternak Abadi livestock farm in Pagar Merbau District, North Sumatra, Indonesia, established in 2012 within an oil palm plantation. The study observed 40 lactating Murrah buffaloes. Variables included milk production and quality metrics such as total plate count in colony-forming units per ml, water content percentage, total solids, fat content, and pH. Results demonstrated a milk yield standardized to 305 days ranging from 1,200.78±490.25 to 1,505.71±589.73 kg/head/lactation. The total plate count was 2.1 x 105 ± 0.32 CFU/ml, total solids were 16.87% (w/w), fat was 5.7% (v/v), and pH was 6.73. The results confirmed that the raw Murrah buffalo milk from the observed farm meets the Indonesian National Standard for milk quality. A significant positive correlation was found between water content and total bacterial colony count (r = 0.82, p < 0.01), suggesting that higher water content in milk correlates with increased bacterial colonies.

The benefits of integrated pest management for apple depend on pest type and production metrics

The development of integrated pest management (IPM) strategies, aimed at reducing pesticide use, has myriad ecological and agronomic benefits to terrestrial ecosystems and the environment, but can also lead to different biological and economic outcomes depending on the production system. The most common facet of IPM in apple is the reduction and/or alternative use of pesticides but also includes cultural, mechanical and biological controls. Using apple as a model system, we performed a meta-analysis of 55 studies from 20 countries to quantify the effects of IPM on beneficial invertebrates, pest and disease pressure, and crop productivity (i.e., fruit yield and quality). We also explored different feeding guilds (i.e., tissue-chewing, sap-sucking or boring/mining herbivores, and beneficial natural enemy predators or parasitoids) to determine whether invertebrate responses to IPM differ between feeding strategies. By scoring IPM adoption based on the relative number of facets of IPM used in each study, we also determined whether the level of IPM implemented in apple farming systems alters the responses of invertebrates and pathogens. Our results demonstrate how IPM adoption increases the performance of natural enemies, while simultaneously reducing pest and disease pressure overall. However, the effects of IPM on disease pressure may depend on the level of IPM adoption because disease pressure increased when multiple facets of IPM were adopted (i.e., as the level of IPM adoption increased). Apple quality was not limited by IPM adoption, yet fruit yield decreased overall. While both natural enemy feeding guilds (predators and parasitoids) responded positively to IPM adoption, only two of the three pest feeding guilds (tissue-chewing and sap-sucking herbivores) decreased under IPM, with boring/mining herbivores showing no response. These results demonstrate the complex benefits and limitations that can occur under IPM and call for economic risk assessments based on these differences. Effective IPM strategies rely on monitoring practices and pest/pathogen prevention but can provide real environmental value.

On restricted Falconer distance sets

Abstract We introduce a class of Falconer distance problems, which we call of restricted type, lying between the classical version and its pinned variant. Prototypical restricted distance sets are the diagonal distance sets, k-point configuration sets given by $$ \begin{align*}\Delta^{\mathrm{diag}}(E)= \{ \,|(x,x,\dots,x)-(y_1,y_2,\dots,y_{k-1})| : x, y_1, \dots,y_{k-1} \in E\, \}\end{align*} $$ for a compact $E\subset \mathbb {R}^d$ and $k\ge 3$ . We show that $\Delta ^{\mathrm{diag}}(E)$ has non-empty interior if the Hausdorff dimension of E satisfies (0.1) $$ \begin{align} \dim(E)> \begin{cases} \frac{2d+1}3, & k=3, \\ \frac{(k-1)d}k,& k\ge 4. \end{cases} \end{align} $$ We prove an extension of this to $C^\omega $ Riemannian metrics g close to the product of Euclidean metrics. For product metrics, this follows from known results on pinned distance sets, but to obtain a result for general perturbations g, we present a sequence of proofs of partial results, leading up to the proof of the full result, which is based on estimates for multilinear Fourier integral operators.

Individual Radiologist Productivity Metrics: Counterpoint-No Individual Metrics Should Be Shared.

Individual Radiologist Productivity Metrics: Counterpoint-No Individual Metrics Should Be Shared.

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

The hidden linkage of corporate efficiency and green innovation with human resource management practices: a newly perspective from China.

It is disappointing that despite having access to human resources at very little cost, rising countries' administrations are so ineffective. It is often believed that an organization's unheralded employees hold the keys to its success or failure. A person's mood and demeanor may be influenced by a variety of factors. Human resource management (HRM) methods have been created and used by scientists all over the globe in an effort to maximize the potential of their most important asset. Eco-friendly inventions are crucial to the survival of humanity and the prosperity of enterprises throughout the world. Therefore, the purpose of this research is to look at how green innovation affects both the environment and businesses. Green process metrics and green product metrics, for example, may have an impact on green innovation, which is why these metrics are important to track. The second primary goal of this research is to learn how the commitment and HR practices of top management shape the link between green innovation and economic and environmental success. A self-reported poll was taken by 370 employees in China's manufacturing industry. A partial least square structural equation modelling was used to examine the data collected between April 2021 and February 2022. This research employed confirmatory component analysis, a standard technique of structural equation modelling (SEM) for examining both overt and covert variables and indicators, to assess the reflecting indicators measurement model. The findings suggest that HRM has an effect on green innovation that is both positive and statistically significant. The environment and the economy benefit greatly from green innovations. Relationships between HRM and green innovation have no unfavorable implications on business results or the environment. However, human resource management has the potential to boost organizations' prosocial impact. In a three-way interaction (moderated moderation) model, organizational success is found to have a significant role, whereas environmental and organizational success play just a little one. To improve economic and environmental outcomes across all sectors, this study adds to the existing body of knowledge and argues for the wider implementation of green innovation practices. Human resource managers play a crucial role in developing a company's culture and values. The findings suggest that with leadership's blessing, green technologies may spread across a company.

Dietary selenium levels affect mineral absorbability, rumen fermentation, microbial composition and metabolites of the grazing sheep

Selenium (Se) is an essential trace element for the growth of all livestock. However, little is known about the effects of dietary selenium levels on production metrics, nutrient digestion and absorption, ruminal microbiota, and metabolites in grazing sheep. Therefore, in this research, 28 individually-housed grazing sheep were randomly divided into a low-Se feeding group (LSe) and a control group for the low-Se feeding phase (LCG), supplemented with multi-nutrient salts containing 0 mg Se and 0.63 mg Se, respectively, for 53 days. After 53 days of feeding, a 7-day digestion test was conducted, then seven sheep in each group were randomly slaughtered. Then, the remaining sheep in LSe and LCG were transferred to a high-Se supplementation group (SSe) and a control group for the high-Se feeding phase (SCG), supplemented with multi-nutrient salts containing 1.90 mg and 0.63 mg Se, respectively. This phase consisted of a 34-day diet adaptation period, a 7-day digestive test, and eventual slaughter. LSe reduced and elevated ruminal Se and copper (Cu) content, respectively, and decreased Se apparent absorbability compared to LCG (P < 0.05). Conversely, SSe reduced and increased ruminal Cu and Se content, respectively, and increased Se apparent absorption in comparison to SCG (P < 0.05). It also increased the ruminal NH3-N concentration (P < 0.05). Amplicon sequencing analysis revealed a decrease in Lentimicrobium and Treponema abundance and an increase in Lawsonibacter and Sporobacter abundance (P < 0.05) in LSe group. Metabolomic analysis confirmed an elevated level of ruminal citrulline, ornithine and glutathione affecting amino acid and glutathione metabolic pathways (P < 0.05) in LSe group. In SSe group, Alistipes were significantly reduced (P < 0.05) with metabolomics showing elevated levels of L-carnitine and arginine, affecting ruminal antioxidant capacity and arginine biosynthesis pathways (P < 0.05). Notably, both treatments had no significant effect on production metrics and apparent digestibility of organic nutrients (P > 0.05). In conclusion, Se deficiency affects Se absorption and amino acid metabolism in grazing sheep, while supplementing Se in diet effectively ameliorates these deficiencies and improves ruminal antioxidant capacity.

Electrochemical sulfate production from sulfide-containing wastewaters and integration with electrochemical nitrogen recovery

Electrochemical methods can help manage sulfide in wastewater, which poses environmental and health concerns due to its toxicity, malodor, and corrosiveness. In addition, sulfur could be recovered as fertilizer and commodity chemicals from sulfide-containing wastewaters. Wastewater characteristics vary widely among wastewaters; however, it remains unclear how these characteristics affect electrochemical sulfate production. In this study, we evaluated how four characteristics of influent wastewaters (electrolyte pH, composition, sulfide concentration, and buffer strength) affect sulfide removal (sulfide removal rate, sulfide removal efficiency) and sulfate production metrics (sulfate production rate, sulfate production selectivity). We identified that electrolyte pH (3 × difference, i.e., 25.1 to 84.9 μM h−1 in average removal rate within the studied pH range) and sulfide concentration (16 × difference, i.e., 82.1 to 1347.2 μM h−1 in average removal rate) were the most influential factors for electrochemical sulfide removal. Sulfate production was most sensitive to buffer strength (6 × difference, i.e., 4.4 to 27.4 μM h−1 in average production rate) and insensitive to electrolyte composition. Together, these results provide recommendations for the design of wastewater treatment trains and the feasibility of applying electrochemical methods to varying sulfide-containing wastewaters. In addition, we investigated a simultaneous multi-nutrient (sulfur and nitrogen) process that leverages electrochemical stripping to further enhance the versatility and compatibility of electrochemical nutrient recovery.

High temperatures and low soil moisture synergistically reduce switchgrass yields from marginal field sites and inhibit fermentation

Abstract‘Marginal lands’ are low productivity sites abandoned from agriculture for reasons such as low or high soil water content, challenging topography, or nutrient deficiency. To avoid competition with crop production, cellulosic bioenergy crops have been proposed for cultivation on marginal lands, however on these sites they may be more strongly affected by environmental stresses such as low soil water content. In this study we used rainout shelters to induce low soil moisture on marginal lands and determine the effect of soil water stress on switchgrass growth and the subsequent production of bioethanol. Five marginal land sites that span a latitudinal gradient in Michigan and Wisconsin were planted to switchgrass in 2013 and during the 2018–2021 growing seasons were exposed to reduced precipitation under rainout shelters in comparison to ambient precipitation. The effect of reduced precipitation was related to the environmental conditions at each site and biofuel production metrics (switchgrass biomass yields and composition and ethanol production). During the first year (2018), the rainout shelters were designed with 60% rain exclusion, which did not affect biomass yields compared to ambient conditions at any of the field sites, but decreased switchgrass fermentability at the Wisconsin Central–Hancock site. In subsequent years, the shelters were redesigned to fully exclude rainfall, which led to reduced biomass yields and inhibited fermentation for three sites. When switchgrass was grown in soils with large reductions in moisture and increases in temperature, the potential for biofuel production was significantly reduced, exposing some of the challenges associated with producing biofuels from lignocellulosic biomass grown under drought conditions.

NSGA families for solving a dual resource-constrained problem to optimize the total tardiness and labor productivity in the spirit of sustainability

This proposed study deals with a typical dual-objective multi-task simultaneous supervision dual resource constrained (MTSSDRC) problem to optimize total tardiness and labor productivity. The problem is modeled mathematically into mixed-integer non-linear programming (MINLP) because the productivity metric is non-linear. The model is converted into a mixed-integer quadratic problem (MIQP), so it can be solved using the hierarchical approach inside a solver. The development of the new problem model and study of the solver’s performance is the first contribution to this proposal. Unfortunately, the solver is not practicable if the number of jobs is large, for example, 50 jobs only. Therefore, we proposed four metaheuristic techniques distinguished by their techniques and decoding schemes as the other contribution. We implement and modify two famous NSGA techniques, namely NSGA-II and NSGA-III, that each is equipped with two decoding scheme types which are D1 and D2. Both decoding schemes are different in the resource allocation sequence and the selection criterion. We compare the performance among metaheuristics by running them in 8 cases of small problems and 28 cases of large problems. The essential results indicate that both D1 and D2 are recommended to be implemented since D1 is more sensitive to the total tardiness objective, but D2 is better at getting solutions with better productivity metrics. Although there is no significant difference between NSGA-II and NSGA-III, we suggest using NSGA-III for practicians because it can spread solutions more balanced, indicating more varied alternatives.