Sampling Period Research Articles

A century of wild bee sampling: historical data and neural network analysis reveal ecological traits associated with species loss.

We analysed the wild bee community sampled from 1921 to 2018 at a nature preserve in southern Michigan, USA, to study long-term community shifts in a protected area. During an intensive survey in 1972 and 1973, Francis C. Evans detected 135 bee species. In the most recent intensive surveys conducted in 2017 and 2018, we recorded 90 species. Only 58 species were recorded in both sampling periods, indicating a significant shift in the bee community. We found that the bee community diversity, species richness and evenness were all lower in recent samples. Additionally, 64% of the more common species exhibited a more than 30% decline in relative abundance. Neural network analysis of species traits revealed that extirpation from the reserve was most likely for oligolectic ground-nesting bees and kleptoparasitic bees, whereas polylectic cavity-nesting bees were more likely to persist. Having longer phenological ranges also increased the chance of persistence in polylectic species. Further analysis suggests a climate response as bees in the contemporary sampling period had a more southerly overall distribution compared to the historic community. Results exhibit the utility of both long-term data and machine learning in disentangling complex indicators of bee population trajectories.

Return and volatility connectedness between agricultural tokens and us equity sectors

This study investigates the return and volatility interconnectedness between emerging digital assets, specifically agricultural tokens, and U.S. equity sectors using the TVP-VAR model. Analyzing data from August 7, 2020, to January 2, 2024, the findings indicate modest interconnections between agricultural tokens and U.S. sectors, with time-varying behavior. Notably, return interconnectedness is generally stronger than volatility, except at the sample period's outset, where volatility dominates. Return spillovers predominantly drive the connectedness system, though agricultural tokens uniquely act as net recipients of both return and volatility spillovers, while U.S. equity sectors mainly transmit spillovers. Optimal portfolio analysis, utilizing portfolio weights and hedge ratios, reveals that incorporating agricultural tokens offers portfolio diversification benefits and enhances hedging performance. Investors are advised to frequently adjust portfolios to maximize diversification and hedging gains. These findings provide significant portfolio implications for policymakers, market participants, and investors.

Are USDA Forecasts Optimal? A Systematic Review

AbstractThe goal of this paper is to systematically review the literature on United States Department of Agriculture (USDA) forecast evaluation and critically assess their methods and findings. The fundamental characteristics of optimal forecasts are bias, accuracy and efficiency as well as encompassing and informativeness. This review revealed that the findings of these studies can be very different based on the forecasts examined, commodity, sample period, and methodology. Some forecasts performed very well, while others were not very reliable, resulting in forecast specific optimality record. We discuss methodological and empirical contributions of these studies as well as their shortcomings and potential opportunities for future work.

Some biological aspects of chacunda gizzard shad, Anodontostoma chacunda (Hamilton, 1822) from the Bay of Bengal, Bangladesh

Anodontostoma chacunda is a commercial fish species in the market and is usually consumed in fresh, smoked, and dried forms. This study investigated the population structure, length-weight relationships (LWR), condition factor (KF), size at sexual maturity (Lm), and breeding season of A. chacunda in the Bay of Bengal, Bangladesh, using the data collected from January to December 2020. A total of 1061 individuals were collected in the sampling period with the help of fishers. Total length (TL) and body weight (BW) were measured using a measuring board and an electronic balance. The TL ranged from 12.50 to 26.70 cm. The allometric coefficient (b) value was 3.34, indicating positive allometric growth in this species. The fish is in good condition, evidenced by its condition factor (KF) ranging between 0.88 and 1.73. Maturity size ranged from 15.40 to 16.80 cm (TL) based on three distinguished models. Spawning time lasts from January to May and from September to December. The peak spawning time was December of A. chacunda in the Bay of Bengal. This information could prove valuable for fisheries researchers and biologists, facilitating efforts toward the conservation and sustainable management of this species.

Age-of-Event Aware: Sampling Period Optimization in a Three-Stage Wireless Cyber-Physical System With Diverse Parallelisms

Age-of-Event Aware: Sampling Period Optimization in a Three-Stage Wireless Cyber-Physical System With Diverse Parallelisms

Understanding zinc isotopic signatures in volcanic lakes

Magmatic activity can be monitored through the chemical variations of volcanic lakes. While those lakes can be influenced by direct magmatic input, water-rock interaction processes in hydrothermal system also have a large influence. Disentangling those effects is critical to use crater lakes for volcanic activity monitoring purposes. Zinc is a volatile metal that is easily degassed and can be incorporated into fluid circulation. During degassing, Zn isotopes can be fractionated and can thus be a potential tracer of magmatic input. Here we present a novel approach using stable Zn isotopic systems to understand how dissolution/precipitation processes affect isotopic compositions of volcanic lakes and if they can be used as tracers of magmatic activity. We investigate Zn isotopes expressed as δ66Zn for four volcanic lakes ranging from hyperacidic to neutral waters. Two of them, Kawah Ijen (Indonesia) and Santa Ana (El Salvador) volcanic lakes have been sampled from 2010 to 2012 and 2000–2002, respectively, and show a clear magmatic input with extremely low pH [0.1–1]. Taal (Philippines, pH ≈ 3) and Kelud (Indonesia, pH ≈ 6.5) volcanic lakes were sampled from 2017 to 2019 and 2003–2007 respectively, and are dominated by hydrothermal rather than magmatic inputs. Hyper-acidic lakes show minimal isotopic variation over the investigated time ranges, with δ66Zn values similar to their basalt-andesitic host rocks (0.26 ± 0.08 ‰). In contrast, we observe large δ66Zn fractionation for the Taal waters (0.4 ± 0.01 ‰) compared to their basalt-andesitic host rocks but no δ66Zn temporal variations. Kelud waters show significant δ66Zn (−0.08 ± 0.06 ‰ to 0.43 ± 0.06 ‰) temporal variation. This could be related to an evolution of the lake water chemistry as the sampling period occurred between two increases in lake temperature culminating into a lava dome eruption in 2007. For Taal and Kelud volcanic lakes, enrichment factors (EF) and PHREEQc modelling indicate that Zn is depleted and suggest that Zn could precipitate in the hydrothermal system, hence fractionating Zn isotopes during these precipitation processes. This contrasts to Zn elemental and isotope values from Kawah Ijen and Santa Ana with an EF ≈ 1 suggesting that Zn is derived from host rock dissolution, in agreement with their δ66Zn. These data are still limited and a longer observation is needed in the future to highlight significant geochemical changes in volcanic lakes and their relation to volcanic activity.

Zero-Order-Hold Triggered Control of a Chain of Integrators with an Arbitrary Sampling Period

Zero-Order-Hold Triggered Control of a Chain of Integrators with an Arbitrary Sampling Period

Diagnosing Radial Ventilation in Dropsonde Observations of Hurricane Sam (2021)

Abstract This study presents a method to diagnose radial ventilation, the horizontal flux of relatively low-θe air into tropical cyclones, from dropsonde observations. We used this method to investigate ventilation changes over three consecutive sampling periods in Hurricane Sam (2021), which underwent substantial intensity changes over 3 days. During the first and last periods, coinciding with intensification, the ventilation was relatively small due to a lack of spatial correlation between radial flow and θe azimuthal asymmetries. During the second period, coinciding with weakening, the ventilation was relatively large. The increased ventilation was caused by greater shear associated with an upper-level trough, tilting the vortex, along with dry, low-θe air wrapping in upshear. The spatial correlation of the radial inflow and anomalously low-θe air resulted in large ventilation at mid- to upper levels. Additionally, at low to midlevels, there was evidence of mesoscale inflow of low-θe air in the stationary band complex. The location of these radial ventilation pathways and their effects on Sam’s intensity are consistent with previous idealized and real-case modeling studies. More generally, this method offers a way to monitor ventilation changes in tropical cyclones, particularly when there is full-troposphere sampling around and within a tropical cyclone’s core. Significance Statement Ventilation, the injection of relatively dry and/or cool air into a tropical cyclone, may weaken a storm. In contrast, the lack of ventilation is favorable for intensification. The purpose of this study is to present a method to diagnose ventilation using aircraft dropsonde observations. Using dropsonde observations collected in Hurricane Sam (2021), there was a period of increased lateral ventilation in two regions around the storm that coincided with when the storm rapidly weakened. The results suggest that monitoring ventilation from dropsonde observations, when available, may be useful for anticipating ventilation-induced intensity changes in tropical cyclones and further studying ventilation pathways.

Long-term surveillance of SARS-CoV-2 in the school community from Campo Grande, Brazil

BackgroundThe COVID-19 pandemic has significantly impacted education systems worldwide, with Brazil being one of the countries with the longest school closures. Over a million children and teenagers have been affected, leading to increased hunger and nutritional deficiencies. This study aimed to implement long-term surveillance of SARS-CoV-2 infections in public and private schools in Campo Grande, Brazil, after returning to in-person classes.MethodsThe study involved testing and genomic surveillance at 23 public and private schools in Campo Grande, Mato Grosso do Sul, Brazil, from October 18, 2021 to November 21, 2022. The participants eligible for enrollment were students aged 6–17 years and staff members from school institutions. At the time of collection, participants were asked if they had symptoms in the last two weeks. Whole-genome sequencing of SARS-CoV-2 was conducted to identify circulating variants and to compare them with those detected in the municipality. The demographic data and clinical history of the participants were described, and a logistic regression model was used to understand how the RT-qPCR results could be related to different characteristics.ResultsThe study included 999 participants, most of whom were women. A total of 85 tests were positive, with an overall positivity rate of 3.2%. The dynamics of case frequency were consistent with those observed in the municipality during the study period. The most common symptoms reported were cough, rhinorrhea, headache, and sore throat. Symptoms were significantly associated with SARS-CoV-2 infection. Eleven lineages were identified in school community samples, with a frequency of occurrence per period similar to that found in the sequences available for the municipality. The most prevalent lineages within the sampling period were BA.2 (59.3%) and BA.5 (29.6%).ConclusionsOur findings demonstrate that schools can play a crucial role in epidemiological surveillance, helping trigger rapid responses to pathogens such as SARS-CoV-2. Long-term surveillance can be used to track outbreaks and assess the role of children and adults in transmission. It can also contribute to pandemic preparedness, enabling a rapid response to emergencies, such as COVID-19.

A novel combination prediction model of ultra-short-term wind speed

The accurate prediction of ultra-short-term wind speed has important theoretical significance and practical application value. In this paper, a combination prediction model of ultra-short-term wind speed based on variational mode decomposition is proposed. Firstly, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed and obtain the components of different frequency. The approximate entropy algorithm is used to calculate the complexity of each component. According to the calculation results, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is used to predict low complexity components. Then, the predicted values of each component are added to get the final prediction result. Gray wolf algorithm is used to optimize the model parameters of support vector machine and echo state network. In addition, the approximate entropy calculation results show that compared with the original ultra-short-term wind speed, the complexity of the components obtained by the variational mode decomposition algorithm is reduced, which makes the modeling of the ultra-short-term wind speed system simpler. Finally, the validity of the model is verified by taking the ultra-short-term wind speed data of 5 minutes and 10 minutes sampling period actually collected as the research object. The results show that the prediction model proposed in this paper has better performance than other single or combination prediction models.

Factor timing in the Chinese stock market

AbstractI conduct an exploratory study about the feasibility of factor timing in the Chinese stock market, covering 24 representative and well‐identified risk factors in 10 categories from the literature. The long–short portfolio of short‐term reversal exhibits strong out‐of‐sample predictability, which is robust across various models and all types of predictors. This predictability is significant both statistically and economically, with a simple investment strategy obtaining its return three times higher than the buy‐and‐hold return in the sample period and a significant annualized 20.4% CH‐3 alpha. Portfolio historical volatility and market volatility measurement predictors play crucial roles in the reversal factor premium's robust predictability. However, such results are not evident in predicting all other factors' long–short portfolios as well as all factors' long‐wing and short‐wing portfolios, and this failure cannot be attributed to their exposure to unpredictable market returns.

Substitution of Methods in Suicide Deaths - Firearm Injury and Hanging.

Background: Prevention strategies for suicide emphasize restrictions on firearm access. These restrictions may lose efficacy if individuals substitute other lethal suicide methods. Aims: The objective of this study is to determine the extent to which individuals who die by suicide in the United States substitute hanging for firearm injury. Methods: This study is a repeated cross-sectional analysis of suicide deaths in the United States from 2003 to 2021. Multiple regression was used to estimate the effect of firearm access proxies on individual suicide methods (hanging vs. firearm injury). Results: The probability of death by hanging was significantly and negatively associated with proxies of firearm access. Limitations: This study does not compare crude rates of suicide by state, which rise on average with rates of firearm ownership. The National Violent Death Reporting System expanded over the sample period, so early years have incomplete ascertainment. Rates of gun ownership and gun safety law scales are measured as proxies. Conclusions: Although means restriction around firearm access is a critical tool for suicide prevention, complementary strategies for prevention around hanging merit further study.

Trends and cycles during the COVID-19 pandemic period

We perform several trend-cycle decompositions through the lens of two unobserved components models, herein estimated for Portugal and the euro area. Our procedure copes with the COVID-19’s consequences by explicitly considering potentially larger second moments during that period. This is achieved through a set of pandemic-specific shocks affecting only the 2020–21 period and embedded into estimation through a piecewise linear Kalman filter. Our methodology generates negligible historical revisions in key smoothed variables when the sample period is expanded until 2021:4, since pandemic shocks absorb a great deal of data volatility with minimal impacts on filtered data revisions or estimated parameters. Furthermore, non-pandemic shock volatility remains largely unaffected by the pandemic period. Innovations affecting the cycle in our preferred model are the key propellers of GDP developments during the COVID-19 pandemic period.

DNA fragmentation of lymphocytes and sperm cells induced by nickel released from orthodontic archwires: A preliminary study.

Orthodontic brackets and archwires placed intraorally are subject to corrosion, leading to the release of cytotoxic metal ions. The aim of this study was to determine whether the use of orthodontic NiTi archwires increases systemic Ni levels and cause alterations on the DNA of cells unrelated to the oral environment such as lymphocytes and sperm cells. Human urine, semen and blood samples were collected before (baseline) sham placement of orthodontic archwires and 15 and 30 days after placement. Lymphocytes and sperm cells cells were evaluated by comet assay. Ni concentration levels in urine increased significantly between baseline and 15 days (p<0.01) and 15 and 30 days of exposure (p<0.01). Progressive decrease in sperm viability and motility was observed between the sampling periods. Lymphocytes and sperm cells showed DNA fragmentation. The increase in systemic concentration of nickel induced structural damage in the DNA of lymphocytes and human sperm cells.

New mechanisms for increasing agricultural total factor productivity: Analysis of the regional effects of the digital economy

The digital economy has introduced new ideas and methods for the high-quality development of agriculture, becoming a driving force for enhancing agricultural production efficiency. This manuscript utilizes balanced panel data from 1,503 counties and employs the three-stage DEA-Malmquist method to measure agricultural total factor productivity (hereinafter referred to as ATFP). Fixed effect models and mediation models are used to explore the impact and mechanisms of digital rural development on ATFP. The research findings indicate that both ATFP and digital rural development are increasing, although regional disparities exist. The eastern region has higher levels of digital rural development and ATFP compared to the central and western regions. Analysis using fixed effect models and mediation models shows that digital rural development has a significant positive effect on ATFP during the sample period, with rural inclusive finance and human capital playing a mediating role. The impact of digital rural development on ATFP varies across regions. It is more prominent in main grain-producing and main grain-consuming areas, and less significant in balanced production and consumption areas. Geographically, the impact is stronger in the eastern region than in the central and western regions. When categorized by the type of agricultural production, the impact is significant in livestock areas, while relatively smaller in planting industry areas. Based on these findings, it is recommended that local areas tailor their approach to digital rural construction and formulate agricultural production policies that leverage regional strengths and address weaknesses. Additionally, promoting the development of rural inclusive finance, cultivating digital talent, improving talent introduction policies, and expanding the rural digital talent pool are suggested to build momentum for agricultural development.

ESG performance and analyst recommendations: evidence from sustainability analysts in the Chinese market

PurposeIn this study, we examine the influence of a firm’s environmental, social, and governance (ESG) performance on analysts’ stock recommendations and earnings forecast accuracy in the Chinese context.Design/methodology/approachWe take a textual analysis approach to analyst research reports issued between 2010 and 2019, and differentiate between two distinct analyst categories: “sustainability analysts,” which refer to those more inclined to incorporate ESG information into their analyses, and “other analysts.”FindingsOur evidence indicates that sustainability analysts tend to be significantly more likely than others to provide positive stock recommendations and demonstrate enhanced accuracy in forecasting earnings for companies with superior ESG performance. Our additional analyses reveal that this finding is particularly prominent for analysts who graduated from institutions emphasizing the protection of the environment, those recognized as star analysts, those affiliated with ESG-oriented brokerages, and forecasts made by analysts in the later part of the sample period. Our findings further indicate that sustainability analysts exhibit a more pronounced negative response when confronted with a negative ESG event.Originality/valueIn general, the evidence from this study reveals the interplay between ESG factors and analyst behavior, offering valuable implications for both financial analysts and sustainable investment strategies.

Exploration of a brain-inspired photon reservoir computing network based on quantum-dot spin-VCSELs

Based on small-world network theory, we have developed a brain-inspired photonic reservoir computing (RC) network system utilizing quantum dot spin-vertical-cavity surface-emitting lasers (QD spin-VCSELs) and formulated a comprehensive theoretical model for it. This innovative network system comprises input layers, a reservoir network layer, and output layers. The reservoir network layer features four distinct reservoir modules that are asymmetrically coupled. Each module is represented by a QD spin-VCSEL, characterized by optical feedback and optical injection. Within these modules, four chaotic polarization components, emitted from both the ground and excited states of the QD Spin-VCSEL, form four distinct reservoirs through a process of asymmetric coupling. Moreover, these components, when emitted by the ground and excited states of a driving QD spin-VCSEL within a specific parameter space, act as targets for prediction. Delving further, we investigated the correlation between various system parameters, such as the sampling period, the interval between virtual nodes, the strengths of optical injection and feedback, frequency detuning, and the predictive accuracy of each module’s four photonic RCs concerning the four designated predictive targets. We also examined how these parameters influence the memory storage capabilities of the four photonics RCs within each module. Our findings indicate that when a module receives coupling injections from more than two other modules, and an RC within this module is also subject to coupling injections from over two other RCs, the system displays reduced predictive errors and enhanced memory storage capacities when the system parameters are fixed. Namely, the superior performance of the reservoir module in predictive accuracy and memory capacities follows from its complex interaction with multiple light injections and coupling injections, with its three various PCs benefiting from three, two, and one coupling injections respectively. Conversely, variations in optical injection and feedback strength, as well as frequency detuning, introduce only marginal fluctuations in the predictive errors across the four photonics RCs within each module and exert minimal impact on the memory storage capacity of individual photonics RCs within the modules. Our investigated results contribute to the development of photonic reservoir computing towards fast response biological neural networks.

Impact of climate risk on energy market risk spillover: Evidence from dynamic heterogeneous network analysis

Using meteorological monitoring data from eight climate disasters across the globe, the Climate Policy Uncertainty (CPU) Index, and the financial data of eight energy markets between 2010 and 2020, this study employs the fixed base extreme difference entropy method to construct a Climate Risk Index (CRI) and investigates the impact of climate risk on the risk spillover among energy markets based on dynamic heterogeneous network analysis. The results confirm that the CRI fluctuated periodically and exhibited an upward trend during the sample period. In terms of risk spillover, the oil, geothermal, and fuel cell markets act as risk receivers, whereas the gas, wind, and bioenergy markets functions as risk emitters. Climate risk significantly increases the network complexity between energy markets and the risk spillover of gas, solar, water, and wind markets but decreases the risk spillover of oil, bioenergy, fuel cells, and geothermal markets. Additionally, the fossil fuel market is more sensitive to climate risk than is the renewable energy market.

Leader-Following Secure Output Consensus of Heterogeneous Multiagent Systems Based on Two Sampling Mechanisms Under Hybrid Cyber-Attacks.

This article investigates the leader-following secure output consensus (LFSOC) problem of the heterogeneous multiagent systems (MASs) under the hybrid cyber-attacks. A novel hybrid cyber-attack model consisting of aperiodic additive deception (AAD) attacks and aperiodic denial of service (ADoS) attacks is proposed for characterizing cyber-attacks in a real network, where the aperiodicity is reflected in the fact that the duration of each cyber-attack can be different. First, a compensator is introduced for each agent to estimate the leader's state. Second, two sampling mechanisms consisting of a multirate sampling (MRS) mechanism and a periodic sampling mechanism are employed for heterogeneous MASs. The MRS mechanism is used to obtain real-time sampled data on the different physical variables of each agent. The periodic sampling mechanism is applied to sample the agents' compensators and the sampled data are broadcast to their neighbors immediately through a network. By selecting an appropriate sampling period (i.e., a communication period of the compensators), the robustness of heterogeneous MASs against hybrid cyber-attacks can be enhanced. Then, the appropriate communication period is selected for the compensators in different network environments by taking into consideration the cyber-attack parameters. Under these two sampling mechanisms, a sync controller is developed to achieve the LFSOC of heterogeneous MASs. Finally, an example is presented to verify the effectiveness of the proposed approach.

Threshold inflation and relative price variability – a proposal for inflation targeting in Lebanon

PurposeThe study investigates the impact of inflation on the variability of relative prices in the context of Lebanon.Design/methodology/approachUnlike the traditional method, which relies on the variance of cross-sectional price changes measured at specific points in time to gauge the variability in relative prices, we employ a more appropriate approach. Under this approach, we capture the dispersion in relative prices by estimating how widely (or closely) a set of commodity prices drift apart over a span of time, offering a more comprehensive assessment. Firstly, we employ Johansen’s cointegration test on rolling subsamples to determine the number of statistically significant cointegrating vectors among the prices of 12 major commodity groups. Under this approach, an increase in the number of significant cointegrating vectors indicates a reduction in relative price variability, while a decrease suggests the opposite. Subsequently, we employ ordinary least squares regression to analyze how the fluctuations in inflation affect the variability in relative prices. The sample period ranges from December 2007 to April 2021.FindingsThe empirical results indicate that there exists a certain threshold inflation rate corresponding to which the variability in relative prices is minimized. More importantly, consistent with the theoretical predictions, the results suggest that it is not inflation per se, but the deviation of inflation from the 3% threshold level in either direction that causes higher dispersion in relative prices.Research limitations/implicationsThe empirical findings from this study have crucial implications for the operation of monetary and fiscal policy. In particular, these findings suggest that stabilizing long-term inflation around a certain threshold rate will not only help to anchor inflation expectations effectively but will also minimize the welfare costs associated with inflation.Originality/valueGiven the rising inflationary pressure in the recent past and its welfare costs, the study assumes crucial importance in understating how fluctuations in inflation distort the relative price structure and eventually cause resource misallocations and economic inefficiency.