Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

The magnetic properties of 0.10 mm ultra-thin grain-oriented (UTGO) silicon steel and Fe-based amorphous (FBA) alloy under sinusoidal excitation were measured experimentally, and the magnetic field strength and iron loss of the two materials under different frequencies and magnetic densities were obtained. Based on the measured data, the magnetization and loss characteristics of the two materials were analyzed and compared. Furthermore, two Epstein square ring models of the same specification and different materials were designed, and the reliability of the models was verified. Then, the electromagnetic characteristics of the two Epstein square ring models at higher and lower frequencies were calculated by using the finite element method, and the iron loss was obtained and compared. The results show that the FBA alloy has good application characteristics at low frequency and low power, and the 0.10 mm UTGO silicon steel has good application characteristics at high frequency and high power. This research can provide important data basis to promote the application of these two materials in new energy equipment.

This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm averages the history of these events over preceding days, in particular the gaps when the events did not occur. The gaps are measured for the clustered events only and can be used to create a description that is quite unique. Results suggest that seismic events can in fact be traced using this key and subsequently recognised again, if the same conditions reoccur. They also suggest that force direction may be more important than magnitude, for this type of earthquake. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.

The definition of UTC in 1972 balanced the needs of the time and frequency community for a universal time scale that was traceable to the definition of frequency in the SI system of units with the needs of the astronomical community for a time scale that was closely linked to UT1, a time scale related to the rotation of Earth. The balance between these two considerations has changed very significantly since 1972, and there is widespread agreement that the method of relating UTC to UT1 should be changed by increasing the maximum tolerance between the two scales to more than the current limit of 0.9 s. I propose to realize this decision by means of an algorithmic adjustment process based on the variation in the length of the day over the previous centuries. I use these historical data to extrapolate the length of the day for a century into the future, and to compute a periodic rate adjustment to UTC based on this extrapolation. The adjustment process will not use time steps. The magnitude of the periodic rate adjustment will be re-considered every century, but the basic adjustment algorithm will not change. I suggest a maximum tolerance between UT1 and UTC that is large enough to absorb the irregular and unpredictable variations in the length of the day with respect to the long-term average increase in this value but plays no role in determining the parameters of the adjustment process that is proposed. I also propose that the adjustment be implemented at 12 UTC on 1 January since that is closest to a universal holiday in all time zones.

The Eastern Kunlun Fault Zone (EKFZ) is located in a seismic blank area. This study investigates the hydrogeochemical characteristics of varied-temperature springs in the EKFZ to understand the relationship between fractures, fluids and earthquakes. It aids in establishing their induction mechanisms and assists in monitoring the earthquake precursors of the EKFZ. In this study, the main elements, trace elements, hydrogen and oxygen isotopes and strontium isotopes of 23 varied-temperature springs in the EKFZ were analyzed. The results reveal the source of groundwater recharge, hydrochemical types, mineral saturation, and for a select few springs indicative of geothermal activity, collected geothermal reservoir temperatures. The study also explores the relationship between earthquakes and springs, identifying sensitive chemical indicators. Based on continuous monitoring at Maqu Station, we found that before the occurrence of the Maerkang Ms 6.0 earthquake, the concentration of Cl− rose by 2.9 times, and 46 days before the Gande earthquake (Ms = 4.7), the concentrations of Na+ and SO42− significantly increased. Finally, a conceptual model of the hydrogeochemistry of springs is presented to describe the groundwater circulation in the study area. This research is of great significance for the analysis of fluid geochemistry in this area and for assessing the seismic hazards of the EKFZ.

Local food has continued to become more popular in both the home and as featured ingredients in all segments of food service. Numerous studies have explored the motivating factors for consumer purchasing motivators. However, no known study has explored if there is a correlation between ego and intent to buy local foods. This study adopted a modified version of the Theory of Trying and surveyed a sample of 237 food service buying agents in South Carolina to examine their perceptions of buying local. Results suggest that ego is a predictor of trying to purchase local foods for restaurants. It was also found that the modified model is an acceptable model for predicting buyer intent. These findings support the majority of the studies looking at ego-centric behaviors with green purchases and could potentially help local food suppliers to better market their products to foodservice establishments. This study could also act as the foundation for future studies to explore the full extent of ego’s motivational effect on purchasing intentions in the food service industry.

Numerous investigations have suggested that seismic source parameters and properties of the surrounding medium harbor valuable information regarding alterations in stress fields and medium properties at the focal depth. Monitoring the spatial and temporal evolution of these parameters can yield insights into variations in stress fields or medium properties within the seismogenic zone, offering a critical avenue to overcome the Earth's inaccessible nature. In recent years, modern seismic parameters such as seismic moment, focal mechanism solution, source stress drop, corner frequency, radiated seismic energy, rupture radius, and apparent stress have been increasingly used to characterize source characteristics. The Sichuan region is situated on the southeastern edge of the Qinghai-Tibet Plateau and is known for its strong eastward extrusion and structural deformation. The main fault zones in the area include the Xianshuihe Fault, Anninghe Zemuhe Fault, and Longmenshan Fault. This paper estimates source parameters of small to medium-sized earthquakes (ML 1.5-5.2) in the main fault zones and adjacent areas in Sichuan. A total of 4,310 earthquake stress drop measurements from 2019 to 2023 were analyzed to create a stress distribution image along the fault. This earthquake parameters training catalog provides insight into how coseismic stresses change and their relationship with other geophysical factors, as well as their spatial and temporal evolution.

The coseismic surface three-dimensional deformation fields were significant for quantified the geometric and kinematic characteristics of earthquake rupture faults. Traditional geodetic techniques were constrained by their intrinsic limitations, such as InSAR, which could only extract far-field deformation fields due to its incoherence, and GNSS, which could only acquire deformations at discrete points. The recently developing optical pixel correlation technique based on high resolution remote sensing images could acquire the near-field coseismic horizontal deformation. In this study, the vertical displacement field of the 2022 Mw 6.6 Menyuan earthquake was obtained by differencing the pre- and post-earthquake DEMs from GaoFen-7 stereo satellite images. Further, the line-of-sight (LOS) and azimuthal (AZI) directions far-field deformations by InSAR, the horizontal near-field deformation by optical pixel correlation based on pre- and post-earthquake GF-2/7 images, and the vertical deformation by differencing the pre- and post-earthquake DEMs, were incorporated to comprehensively solve the complete three-dimensional deformation fields of the 2022 Mw 6.6 Menyuan earthquake. Firstly, the vertical displacement field by differencing DEMs indicated there were significant vertical displacements about 1 m at the bend region, which was induced by the local compressive stress. In addition, at the epicenter on the middle segment of ruptured Lenglongling fault (LLLF), the maximum lifting occurred on the southern sides of the main and secondary faults exceeded 2 m. Secondly, the three-dimensional deformation fields solved by multiple deformation data demonstrated that the near-field deformation field calculated by optical pixel correlation method could quantify the displacements distributed over the rupture fault zone, which were not available on the InSAR deformation maps. Finally, the surface 2D strain derived from the displacement maps calculated by optical pixel correlation revealed high strain concentration on the rupture fault zone. Our study focused on the complete surface three-dimensional deformation estimation from multiple far- and near-field deformation data, and provided a new perspective for a deeper understanding of the characteristics of coseismic surface deformation and the rupture pattern of the fault.

Wave propagation in buildings analyzed from its response to earthquakes can be applied for tracking the dynamic characteristics changes based on impulse response functions. The velocity of traveling shear-waves and intrinsic attenuation in buildings can be retrieved for buildings. It is an approach of importance for system identification. The Factor Building at the University of California, Los Angeles campus (henceforth referred as the UCLA Factor Building), an instrumented 15-story steel-moment frame structure, are selected for dynamic response characteristics identification. They are inferred from wave propagation using seismic interferometry for recorded motions. The deconvolved waves are used to compute shear wave travel time and wave attenuation. The natural logarithm for the envelopes of the waveforms deconvolved with the signal at the basement constrains the attenuation. The waveforms from deconvolution with the motion at the basement suggest the fundamental mode of the building. The frequency and decay with time constrain the shear velocity and attenuation as well. The velocity are measured using arrival times picked from these deconvolved waves and the average velocity is 147.1 m/s. The quality factor is equal to 10.8 with the damping ratio at 5%. The shear wave velocity and damping ratio estimated from the deconvolved waves have a good match with these inferred from the waveforms deconvolved with the basement recording. This consistency suggests that wave deconvolution can be applied to extract the building response from the excitation and ground coupling. Based on the resonant frequencies and damping ratio, the finite element model of the building is updated. This study implies that wave deconvolution is powerful to extract the building dynamic characteristics. It helps to better the understanding of the dynamic response of buildings to earthquakes.

Launched on 2 February 2018, CSES is the first constellation in China to advance our understanding of seismo-ionospheric influence and lithosphere-atmosphere-ionosphere coupling process in electromagnetic environment during earthquake prediction practice. For this purpose, a software platform developed for automatically searching ionospheric perturbations from the CSES plasma data had been put into service immediately after the Wushi MS 7.1 earthquake occurred on 23 January 2024 to weekly detect possible precursory information before impending aftershocks. Effective ionospheric variations were successfully detected two times during more than four weeks. An ionospheric perturbation with an enhanced magnitude 38.3% was primarily detected on electron density but none on ion density on 24 January 2024 at night orbit 33175 and the corresponding variations in different parameters recorded at the same orbit presented a complex features. There irregularities possibly combined with influence both from three following strong aftershocks with magnitudes over 5.0 during 25–30, January and magnetic storm disturbance due to Kp value once up to 3.7 on 24 January. An electron perturbation with an amplitude 24.6%, as well as an ion one with 27.3%, was successfully searched automatically at the same revisiting orbit 33251 occurred on the revisiting date of 3 February 2024 in a magnetic quiet period. These two plasma variations, as well as ones on other ionospheric parameters, were characterized by highly similar properties, which increases the availability as seismic precursors. However, no obvious variations had been observed at other revisiting orbits or other orbits near the aftershock areas during this period till the occurrence of a magnitude 5.3 earthquake on 24 February and the strongest one 5.8 on 25 February, respectively, 20 days after and 1000 km away.

The real-time input of ground motions can effectively assess earthquake disasters in building structures. In this study, we proposed a nearby real-time simulation method that can be applied to assess regional earthquake disasters using structural monitoring recordings based on an urban earthquake simulation system. Real-time accelerations recorded during the 5.1 magnitude Tangshan earthquake were used to update and modify the computing models. El Centro waves were calibrated to 400 cm/s2 for structural seismic resilience analyses. The results indicated that approximately 70% of the structural functions were lost during rare earthquakes. Regional numerical models of 216 buildings were constructed and timeously updated using the Geographic Information System and measured data. The inputting ground motions recorded in the 5.1 magnitude Tangshan earthquake and typical El Centro waves were selected to analyze the structural seismic response, in which the damage indexes were computed, and the damage predictions of regional 216 buildings were also simulated in different levels of earthquakes by being combined with the simplified principles of structural damage estimation. Finally, the evaluation results were visualized in three dimensions using ParaView software. The simulated results of earthquake disasters at an urban scale will promote the prediction abilities of local earthquake administration agencies and have considerable potential to provide essential information for emergency responses and regional disaster mitigation.

Although vitamin D deficiency has been correlated with an increased risk of cardiovascular disease (CVD), there are few data on the association between 25-hydroxyvitamin D (25(OH) D) and atherogenic indices predictive of CVD. This study aimed to investigate the relationship of vitamin D status with lipid profile and atherogenic indices in adult women in Morocco. Three hundred healthy women, aged 18 to 50 years, from Meknes were included. Fasting 25(OH) D and lipid concentrations were determined using a one-step electrochemiluminescence-based immunoassay and an enzymatic method, respectively. Atherogenic indices (Atherogenic index of plasma (AIP), non-HDL cholesterol (Non-HDL-C), Atherogenic coefficient (AC), Castelli risk indices I and II (CRI-I and II), and CHOLIndex (CI)) were calculated using conventional lipid parameters. Logistic regression models and operating characteristic curve (ROC) analysis were used to assess the relationship of the variables and estimate the threshold of 25(OH) D levels associated with high atherogenic indices. 25(OH) D below 20 ng/ml was significantly associated with an enhanced risk of hypertriglyceridemia and elevated values of AIP, AC, Non-HDL-C, and CRI-I with an OR (95% CI) of 4.904 (1.856-12.959); 3.637(2.149-6.158); 3.589(1.673-7.700); 2.074(1.215-3.540), and 2.481(1.481-4.123), respectively. According to ROC analysis, the likelihood of hypertriglyceridemia and high values of AIP, AC, Non-HDL-C, and CRI-I were associated with 25(OH)D thresholds ≤15.15 ng/ml, ≤ 17.5 ng/ml, ≤ 19.8 ng/ml, ≤ 20.1 ng/ml and ≤ 19.5 ng/ml, respectively, all p < 0.01. Based on atherogenic indices, this study indicates that vitamin D below 20 ng/ml may increase the risk of cardiovascular disease in adult women. Additional health efforts are needed to raise awareness among women and healthcare providers in preventing and controlling cardiovascular risk factors.

The NW-SE trending dextral strike-slip faults on the north side of the Tian Shan play an important role in accommodating the crustal shortening, which mainly includes the Karatau fault, Talas-Fergana fault, Dzhalair-Naiman fault, Aktas fault, Dzhungarian fault (also named Bolokenu-Aqikekuduk fault) and Chingiz fault. The classic viewpoint is that the strike-slip fault is the adjustment product caused by the difference of the crustal shortening from west to east. Another viewpoint attributes the dextral strike-slip fault to large-scale sinistral shearing. The Alakol Lake Fault is situated along the northern margin of the Dzhungarian Gate, stretching for roughly 150 km from Lake Ebinur to Lake Alakol. Our team utilized aerial photographs, Satellite Stereo-imagery, and field observation to map the distribution of the Alakol Lake fault. Our findings provided evidence supporting the assertion that the fault is a dextral strike-slip fault. In reference to its spatial distribution, Lake Alakol is situated in a pull-apart basin that lies between two major dextral strike-slip fault lines: the Chingiz and Dzhungarian faults. The Alakol Lake Fault serves as a connecting point for these two faults, resulting in the formation of a mega NW-SE dextral strike-slip fault zone. According to our analysis of the dating samples taken from the alluvial fan, as well as our measurement of the displacement of the riser and gully, it appears that the Alakol Lake fault has a dextral strike-slip rate of 0.8-1.5mm/yr. The strike-slip rate of the Alakol Lake fault is comparatively higher than that of the Chingiz fault in the northern region (~0.7 mm/yr), but slower than that of the Dzhungarian fault in the southern (3.2-5 mm/yr). The Chingiz-Alakol-Dzhungarian fault zone shows a gradual decrease in deformation towards the interior of the Kazakhstan platform. We contend that the rate of deformation observed is better explained by the crustal shortening adjustment model, rather than the large-scale sinistral shearing model.

Earthquake-triggered landslides represent a significant seismic-related disaster, posing threats to both the lives and properties of individuals in affected areas. Furthermore, they can result in road and river blockages, as well as other secondary disasters, significantly impacting post-earthquake rescue efforts. Efficient and accurate rapid assessment of high-risk landslide zones carries important implications for decision-making in disaster response and for mitigating potential secondary disasters. This study uses the high-intensity Zone VII to IX of the 5 September 2022 Luding Ms6.8 earthquake as a case study. The "difference method" and the "cumulative displacement method," based on the simple Newmark model, are employed to assess earthquake-triggered landslides. The evaluation results from both methods demonstrate that the areas posing an ex-tremely high risk of earthquake-triggered landslides are predominantly situated on the western side of the Xianshuihe Fault. Actual landslide data verification shows that both models have high predictive accuracy, with the difference method model slightly outper-forming the cumulative displacement method model. Moreover, the study recommends determining threshold values for each landslide hazard interval with significant physical meaning using past strong earthquake data as soon as possible when utilizing the difference method to assess the risk of earthquake-triggered landslides.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

Background/Objectives: Women's reproductive and overall well-being are intricately associated to vitamin D levels. In Morocco, vitamin D deficiency is a major issue for premenopausal women, yet little data exists on their dietary intake of this nutrient. This study explores vitamin D dietary intake, socio-demographic determinants, and its correlation with serum 25-hydroxyvitamin D [25(OH)D3] in premenopausal Moroccan women. Methods: A cross-sectional study was conducted with 355 women aged 18-49 in Meknes, Morocco, using validated questionnaires to assess dietary intake and sun exposure. Potential covariates such as physical activity and BMI were also assessed . Serum 25(OH)D was measured via chemiluminescence immunoassay. Chi-square tests and Spearman correlation were used to identify socio-demographic factors influencing vitamin D intake. Linear and multivariate regression analysis determined the main predictors of serum 25(OH)D levels. Results :The median (interquartile range) vitamin D dietary intake was 2.8 (2.7) µg/day , with less than 20% of women meeting the recommended 5 µg/day. Major dietary sources were fish (48%), dairy (24.3%), and meat (9.4%), with fortified foods contributing only for 7%. Lower intake was linked to younger age (p=0.004) and rural living (p=0.024). While no significant association was found between dietary intake and serum 25(OH)D ((β(SE): 0.155(0.094), p=0.340 ) , sun exposure positively predicted serum levels(β(SE): 0.343 (0.128), p=0.008) , and BMI negatively affected them (β(SE): -1.128 (0.337), p=0.001). Conclusions: Our findings highlight insufficient vitamin D intake among premenopausal Moroccan women, especially younger and rural women. Nutritional interventions are needed to address this deficiency, promoting better dietary habits, sun exposure, and healthy weight.

Vitamin D and iron deficiencies are the most prevalent micronutrient deficiencies among women of reproductive age (WRA) in Morocco. Recent studies suggest that Vitamin D deficiency (VDD) may reduce iron bioavailability necessary for erythropoiesis, potentially leading to iron deficiency (ID) and anemia. This study investigates the association between vitamin D status, iron levels, and anemia risk among Moroccan WRA aged 18-49 years. A cross-sectional study was conducted with 463 participants. Serum levels of 25(OH)D, blood count parameters, iron, ferritin, C-reactive protein, and creatinine were measured. Lifestyle factors, including dietary intake, sun exposure, and physical activity, were evaluated using validated questionnaires, and anthropometric data were collected. Linear and logistic regression models assessed the relationships between variables, while Receiver Operating Characteristic (ROC) curve analysis evaluated the predictive accuracy of VDD for ID and anemia. VDD (25(OH)D < 20 ng/ml) was significantly associated with lower levels of hemoglobin, hematocrit, red blood cells, and ferritin (all p < 0.01), suggesting a role of vitamin D in erythropoiesis and iron storage. Multivariate logistic regression showed that VDD increased the risk of anemia (OR: 7.17, 95% CI: 3.19-19.28, p < 0.001), ID (OR: 2.20, 95% CI: 1.32-3.77, p = 0.007), and IDA (OR: 4.10, 95% CI: 1.73-12.08, p = 0.004). Dietary iron intake was inadequate among all participants, with minimal protective effect against anemia and ID (β(SE): -0.08(0.03), p = 0.030 and β(SE): -0.05(0.02), p=0.037 respectively). ROC analysis demonstrated moderate discriminative power for VDD in diagnosing IDA and anemia (AUC: 0.643 and 0.603) but reduced accuracy for ID (AUC: 0.575). This study identifies VDD as a significant risk factor for impaired iron status and anemia among Moroccan WRA, underscoring the need for targeted nutritional interventions and further research to explore effective prevention strategies.

Through analysis of the constituent components of the observation data from 12 geoe-lectric field stations located approximately 600 km form the epicenter of the Jishishan Ms6.2 earthquake, this study reveals that during earthquake preparation and occurrence, the geoelec-tric field at each station exhibit non-uniform variations. These variations are characterized by significant decreases or increases in correlation coefficients between different azimuths (δxy) and between long and short polar distances within the same azimuth (δxx). Additionally, there are discrete or extruding changes observed in the dominant azimuth angles of the telluric field at the stations, ranging approximately from 40° to 90°. These anomalies indicate local site effects and exhibit quasi-synchronous temporal characteristics. The study found that stations located in regions with intense tectonic activities, such as Menyuan and Jingtai, experience more significant anomalous changes influenced by the tectonic activities of the Qaidam-Qilian Mountain block. When multiple stations with anomalous changes are used to determine the earthquake epicenter, earthquakes often occur within or near the intersection area of the predicted ranges of these sta-tions. The specific time of earthquake occurrence shows no clear relationship with the locations or tectonic activities of the stations.

To achieve rapid post-earthquake repair of self-centering ribbed floor flat beam frame structures, a ductile hybrid joint consisting of dog bone weakened energy dissipating steel bars connected to the upper and lower column sections through high-strength threads based on the damage control design concept is proposed. By moving the ductile energy dissipating zone out to the locally weakened section of the energy dissipating steel bars and the locally unbonded prestressed steel bars in the core area, the residual deformation was limited and the seismic performance was improved. Based on the working principle of hybrid joints, low cycle loading tests were conducted on two joint specimens to analyze the influence of lateral prestress on the seismic performance of the hybrid joints. Numerical modeling methods were used to compare the position of the energy-consuming steel bars in the composite layer and the friction performance of joints. The research results indicated that the hybrid joint had stable load-bearing, deformation, and energy dissipation capabilities, with damage primarily concentrated on the energy-consuming steel bars. Even at an inter-story displacement angle of 5.5%, the upper and lower column segments remained elastic. After unloading, the connection seam at the joint was closed, and the self-centering performance was good. When the inter-story displacement angle reached 5.5%, the lateral prestress increased from 150 kN to 250 kN, the ultimate bearing capacity of the joint increased by 16.3%, and the cumulative energy consumption increased by 30.0%. The influence of the friction coefficient of the joint surface on the structural performance was set at a threshold of 0.7. When it was less than the threshold, the ultimate bearing capacity and initial stiffness of the joint increased with an increase in the friction coefficient. After reaching the threshold, the increase in the ultimate bearing capacity of the joint slowed down, and the rate of stiffness degradation gradually accelerated. This joint had excellent seismic performance and can also achieve post-earthquake repair of structures.

A robust explicit model predictive control (EMPC)-based frequency adaptive grid voltage sensorless control is developed for a grid-connected inverter (GCI) via a linear matrix inequality (LMI) approach under the model parameter uncertainties and non-ideal grid environment. The control scheme is achieved by an enhanced prediction model rather than the conventional prediction model provided by fixed parameters to maintain the quality of grid currents injected into the grid when the model parameters are varied. Furthermore, an LMI-based observer is integrated with the disturbance observer to improve the reference tracking performance and reject disturbances. The proposed observer is employed for the grid frequency adaptive control without need of grid voltage sensors. The proposed current controller and observer employ the LMI scheme to maintain a stable and robust operation of the GCI. The discrete-time frequency response and pole-zero map analyses are used to investigate system stability and robustness against parameter uncertainties. Comprehensive simulation, experimental results and analyses clearly demonstrate the robustness of the proposed control scheme under various adverse test conditions with unexpected grid and system uncertainties.

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WT), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-time scale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ES, and distributed WT are analyzed. Based on this analysis, then a multi-time scale voltage optimization scheme for distribution networks using MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of proposed method, simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results that are more realistic compared to the day-ahead optimization method.

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarante. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with power and energy balance. Drawing upon the principles of conditional value at risk theory, the risk indexes of load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment and the risk cost of load shedding, a distributed robust optimal scheduling model based on flexible ramp reserve of thermal power is established.

The lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. In response to the insufficient training data of defect images with complex backgrounds such as rust and surface oil leakage in substation equipment, leading to poor performance of the detection model, this paper proposed a novel adversarial deep learning model for substation defect generation: ADD-GAN. In comparison to existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model utilizes a joint discriminator for overall image and defect image to address the issue of low attention to local defect areas, thereby improving the loss of image features. This enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves an mAP of 81.5% on the test dataset, representing an improvement of 9.6% over the original dataset, 5.7% over traditional augmentation methods, and 7% over typical adversarial deep learning methods. This confirms that the ADD-GAN method can generate a high-fidelity dataset of substation equipment defects.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

At 12:28 UTC (8:28 PM, local time) on 15 January 2023, a shallow, moderate earthquake with a magnitude (Mw) of 4.7 and a depth of one (1) kilometer shook the northern part of Leyte Island in the central part of the Philippines. The ground shaking was felt at PHIVOLCS Earthquake Intensity Scale (PEIS) VI, (very strong) equivalent to Modified Mercalli Intensity (MMI) VI in areas along the strike of the Leyte segment of the Philippine Fault. Even though this earthquake is classified as moderate in size, significant geologic, structural, and socio-economic impacts were documented. Most of the earthquake impacts that include surface rupture, liquefaction, and mass movements are limited near the epicentral area. The earthquake produced an 8-km-long surface rupture, the first documented rupture generated by a magnitude <6 along the Philippine Fault, and is unusual in other surface rupturing earthquakes worldwide. It injured 18 persons and damaged 434 residential buildings and 26 public infrastructures which amounts to about 500 thousand US dollars. Structural damages were caused by surface rupture and/or poor engineering construction practices. Analysis of the seismic waveform and recorded peak ground acceleration (PGA) suggests that the subsurface geology influenced the ground motion amplification in an area near the epicenter. Based on the hypocenter, focal mechanism solution, aftershock distribution, and actual field observation, the causative fault for this event is the northern end of the Philippine Fault – Leyte segment. Moderate in size, this earthquake emphasizes the need and importance of documenting moderate-sized earthquakes as a tool and guide for medium and long-term earthquake risk assessment and resiliency.

In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverters-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative to enhance the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state space model and eigenvalues analysis is presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small- signal stability of the entire system. And, the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. Case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations.

The development and utilization of wind energy is of great significance to the sustainable development of China's economy and the realization of the goal of "dual carbon". Under the typhoon system, the randomness and volatility of wind speed become an important basis for affecting the energy efficiency utilization and design of wind turbines. Therefore, based on wind speed and wind direction data at 10m, 30m, 50m and 70m heights of a wind power tower in Yancheng, Jiangsu Province, this paper analyzes the change characteristics of wind speed and wind direction by using BFAST method and Hurst index. Based on typhoon body data, topographic data and mesoscale system wind direction data, the causes of the occurrence and development of wind speed and wind direction of wind tower are further analyzed, and a conclusion is drawn: (1) In BEAST method, there are 5, 5, 6 and 6 change points at the height of 10m, 30m, 50m and 70m respectively. Among them, the change points at the height of 10m, 30m and 50m all change before and after node 325, and the change time point at the height of 70m is inconsistent with other heights. Hurst index results show that the inconsistency at 70m altitude is stronger than that at other altitudes. (2) The wind direction sequence at 10m, 30m, 50m and 70m altitude is fitted by stages, and the direction of wind direction is SE-E-SE-SW-W-NW, where, At the height of 70m, the deviation of the fitting trend line from other altitudes is larger in the wind speed strengthening stage and weakening stage. (3) The wind speed at the height of 10m and 70m has different response degrees to the typhoon body. The correlation between the wind speed and the distance between the wind measurement tower and the typhoon near the center is stronger at the height of 10m than at the height of 70m. The surface type and the wind direction of the mesoscale system also have certain effects on the wind speed and direction. In this paper, the change characteristics and influencing factors of wind speed and direction of wind tower are studied, which provides methods and theoretical support for the study of short-term wind speed when typhoon passes through, and provides reliable support for the selection of wind power forecast indicators and the selection and design of wind turbines under extreme gale weather systems.

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

Although the relations between sleep and academic performance have been extensively examined, how sleep predicts future academic performance (e.g., 2 -3 years) remains to be further investigated. Using wearable smartwatches and a self-report questionnaire, we tracked sleep activities of 45 college students over a period of approximate half a month to see whether their sleep activities predicted their academic performance, which was estimated by grade point average (GPA). Results showed that both nighttime sleep awakening frequency and its consistency in the tracking period were not significantly correlated with the GPA for the courses taken in the semester during sleep tracking (current GPA). However, both nighttime sleep awakening frequency and its consistency inversely predicted the GPA for the rest of the courses taken after that semester (future GPA). Moreover, students with more difficulty staying awake throughout the day obtained lower current and future GPAs, and students with lower inconsistency of sleep quality obtained lower future GPA. Together, these findings highlight the importance of nighttime sleep awakening frequency and consistency in predicting future academic performance and emphasize the necessity of assessing the consistency of sleep measures in future studies.

Significant earthquakes frequently occur in Indonesia. Indonesia is situated over three active tectonic plates, resulting in the formation of faults and trenches on the land and ocean floor. For the last 120 years since 1900, there have been more than 1,250 significant earthquake events in Indonesia. In this study, we analyse Indonesia's significant earthquake events using geostatistical and geovisualisation methods to produce an appropriate geospatial analysis platform using the RShiny package to build the WebGIS application. The results show that the earthquake events were spatially distributed from the Sumatera fault in the western part of Indonesia to the southern part of Indonesia, where the Java trench was located and the eastern part of Indonesia. The WebGIS application received a positive evaluation by respondents, with a mean value of 1.617 for pragmatic quality, 1.808 for hedonic quality, and 1.713 for overall quality. This means that the WebGIS application is of good quality based on respondents' impressions. The users also more easily gained insight into information as a result of geostatistical methods. The information gained by the users during the user interaction with the WebGIS platform overlapped with the information that the researcher started with, that is, the spatial cluster of significant earthquakes in Indonesia.

(1) Background: We aim to construct a machine learning (ML) algorithm to predict the risk of distant metastasis (DM) of T1 and T2gallbladder cancer (GBC); (2) Demographic and clinical pathological data of T1 and T2 GBC patients were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 to develop seven ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1- score, and area under the receiver operating characteristic curve (AUC); (3) Results:A total of 4371 patients were included in the study, of whom 764 (17.4%) developed DM. Multivariate logistic regression showed that age, histology, tumor size, T and N stages were independent factors in GBC with DM. A novel nomogram was established to predict distant metastasis in early T stage GBC patients. Evaluation indicators of the best model Random Forest (RF) were as follows: accuracy (0.828), recall rate (0.862), precision (0.811), F1- score (0.836), and AUC value (0.913); (4) Con-clusions: The RF model constructed in this study could accurately predict distant metastasis in GBC patients, which may provide clinicians with more personalized clinical decision-making recom-mendations.

With the widespread application of Voltage Source Converter (VSC) in the power system with high penetration of renewable resources, the problem of wideband oscillations caused by the interaction between the power converters and the power grid has drawn large attention. This paper presents a new methodology for simplifying the mathematical derivations of the impedance model of VSC. Using the new simplified model, the stability and the spectrum characteristic of the performance of the VSC can be clearly analyzed. The proposed second-order d-q impedance model of the grid connected VSC at presence of PLL, not only mathematically proves that the wideband oscillatory modes always occurs in conjugate pairs, but also deliver a clear vision of the physical essence of the wideband oscillations. The classic modal analysis is executed in the paper to demonstrate the stability and frequency spectral characteristics of the wideband oscillations in the VSC integrated system. The impact of control parameters of the VSC on the wideband oscillatory stability is thouroughly analyzed using the results of the modal analysis. The methodology can be considered a powerful tool for the control design of VSC in the modern power system.

This paper reviews the multiple roles of machine learning in improving the resilience of power grids, especially in applying new energy storage technologies. Energy storage technologies, such as compressed air energy storage, flywheel energy storage, and superconducting coil energy storage, significantly improve the power grid's ability to respond to load fluctuations and emergencies through intelligent control and optimisation of machine learning algorithms. This not only helps to optimise energy dispatch and improve the operational efficiency and flexibility of the grid but also significantly improves the stability and reliability of the grid so that it is better able to meet future challenges and needs.

Abstract Background: Periodontitis is associated with systemic chronic inflammatory diseases. There is limited evidence on the influence of anti-cytokine therapies on the periodontal condition and microbiome in the tooth pocket of such patients, so the aim of this study was to elucidate this issue. Methods: In this observational trial, the periodontal status and the gingival crevicular fluid of 13 patients with different chronic inflammatory diseases were obtained before initiation of anti-cytokine treatment and 14 weeks after. Gingival crevicular fluid was collected for 16S rRNA gene sequencing from a clinically healthy tooth and the deepest measured pocket. Results: In participants with advanced attachment loss at baseline, the clinical attachment level significantly decreased from 2.9 (2.3-3.4) to 2.4 (1.3-3.0) mm (p = 0.04), and the bleeding- on-probing index dropped from 19.4 (10.0-21.0) to 7.3 (6.9-9.9) mm (p = 0.02) without any further periodontal treatment. Shannon’s diversity index significantly increased in the deepest pockets of patients (p = 0.039). Conclusion: The data showed alterations in diversity of the subgingival microbiome and improvement of periodontal parameters over the course of the study, implying a shift towards a healthier condition after starting anti-cytokine therapy. Additional investigations are needed to analyze whether the administration of selective biologicals can improve periodontal conditions in patients with or without chronic inflammatory diseases.

The building envelope has the most significant contribution in the reduction of the building energy consumption. Application of new, alternative and improved materials and systems has an important impact on the buildings performances. This paper is focused on the thermal transmittance, as an indicator of the thermal conductance of the construction element. It includes comparisons of the U-values, calculated by software, with those measured in situ on three representative façade walls. The walls have been constructed with the new wall system Fragmat NZ-1, a new product in Macedonian buildings. This research provides basic information on the thermal transmittance of the system. The results of the analysis show that the in situ measuring is a useful tool in validation of the precision of analytically calculated values, since it was difficult to obtain precise results from the analytical calculations only, especially when the layers are with non-uniform thickness.

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) makes it difficult to accommodate renewable energy with a high proportion. It is thus necessary to excavate its potential flexibility to improve its operational performance and consume renewable energy. This paper unlocks the adjustment potential of flexible buildings and builds a flexible thermal load regulation model according to the dynamic thermal characteristics and thermal comfort elastic interval of the buildings and a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates multiple flexible load regulations and the variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanism in different types of flexible loads. Its results show a saturation effect between the flexible thermal and electrical loads in increasing renewable energy consumption and a synergy effect in decreasing the total cost. This synergy can reduce the total cost by 0.73%. Besides, the total cost can be reduced by 15.13%, and the renewable energy curtailment rate can decrease by 12.08% if the flexible electric and thermal loads are integrated into the operation optimization of HRIES.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

The severe climate changes due to global warming pushed the world to strive to reduce carbon emissions. This study aims to minimize the carbon emissions for a proposed smart energy hub. This paper prioritizes the minimization of carbon emissions, taking into consideration the minimization of operation running costs, and the maximization of profit. In this paper two optimization scenarios were studied, to compare the results. In the first scenario, the minimization of carbon emissions was achieved, in the second scenario, the minimization of running costs and maximization of profit from the hub assets were studied. The proposed model was designed in MATLAB. Then the results were verified by CPLEX and validated by RTDS. The multi-objective model was presented to obtain the optimal operation. The mitigation of data uncertainty was achieved by applying the Kalman filter. In this work, a novel method was proposed for the estimation of the quarter-hour resolution data from the hourly one via the Kalman filter rather than applying the classic polynomial interpolation methods.

We hypothesis that large earthquakes generate Horndeski-like Gravitational Wave (GW). We find that such a Horndeski-like GW propagates with the speed of sound. The sound waves generated by an earthquake make a local and temporal change to the Earth’s diagravitational medium; therefore, they modify the GW speed in a standard, Alternative-Theory-of-Gravity (ATG) sense. The quantum of the Horndeski-like GW is a massless scalar quasiparticle and cannot exist outside of the propagation region of the P-field. The Horndeski-like GW may be detectable by future GW detectors with a sensitivity of 10−15 Hz^−1/2 in the region of 0.1–1 Hz.

Aging causes various degenerative diseases in the older adult population. Senescence, a state of permanent cell-cycle arrest accompanied by the production of various pro-inflammatory factors known as senescence-associated secretory phenotype (SASP), is considered a significant contributor to the aging process and its chronic diseases. Ample evidence showed that various stressors could induce senescence, including DNA damage, telomere shortening and damage, activation of oncogenes, and mitochondrial dysfunction. Numerous credible findings indicate that environmental agents can induce senescence, including UV radiation, a high-fat diet, heavy metal exposure, and cigarette smoke. These findings posed the possibility that many more environmental agents may induce senescence and accelerate aging but remain unidentified. Senescence also becomes more intriguing due to its promising future as a pharmacological target to blunt the detrimental effects of aging and prevent aging-related diseases, either by eliminating the senescent cells or by controlling the SASP. On the other hand, investigating senescence has become more intricate due to the need for a multi-marker approach and translation in vivo analysis. This review will discuss senescence and its biomarkers, how to identify gerontogens in vivo, and also the development of senotherapy that targets senescent cells.

The Huaniushan lead-zinc-silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX). The mining area has complex geological conditions. The main tectonic structure in the area is the Huaheitan-Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan Lead-zinc-silver polymetallic deposit, there are diverse understandings of the deposit's genesis and mineralization law, making it challenging to meet the geological interpretation requirements of the Huaniushan Lead-zinc-silver polymetallic deposit. Moreover, a single geophysical exploration method utilizes limited rock physical parameters, making it difficult to effectively characterize underground structures. The integrated exploration of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of geophysical data inverse interpretation. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological-geophysical models based on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technology systems are the current development trends in mineral exploration. In light of this, this paper conducts research on the three-dimensional inversion interpretation of the heavy magnetoelectric exploration data of the Huaniushan sedimentary exhalative deposit lead-zinc-silver polymetallic deposit in the mining area and constructs a three-dimensional geological-geophysical model of the study area based on the obtained three-dimensional physical structure of the underground three-dimensional density, magnetization intensity, resistivity, and polarizability structure of the study area, in combination with the information of related geology and drilling holes. It comprehensively interprets the favorable mineralization parts in the study area.

This paper proposes an optimization method for the scheduling of a multi-energy virtual power plant supply-demand balance in the power market environment of Jiangxi Province. The objective of this method is to enhance the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method comprehensively considers the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Additionally, real-time market price feedback is integrated to achieve accurate allocation of power supply and demand. Through a case study of a multi-energy virtual power plant in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected through SAR techniques. Two models, generalized linear model (GLM) and random forest (RF), were used to predict the mass of residues using interfer-ometric variables (phase, amplitude and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R2 of 0.48 for GLM and 0.13 for RF), with acceptable bias and RMSE. We concluded that SAR data are feasible to predict the mass of harvesting residues and the findings could lead to improved monitoring and management of forest residues, contributing to sustainable forestry practices and enhanced bioenergy resource utilization.

To achieve n-type doping in diamond, extensive investigations employing first principles have been conducted on various models of phosphorus doping and boron-phosphorus co-doping. The primary focus of this study is to comprehensively analyze the formation energy, band structure, density of states, and ionization energy of these structures. It is observed that within a diamond structure solely composed of phosphorus atoms, the formation energy of an individual carbon atom is excessively high. However, the substitution of two carbon atoms leads to the conversion of diamond into a p-type semiconductor. Upon examining the P-B co-doping structure, it is revealed that the doped impurities exhibit a tendency to form more stable cluster configurations. As the separation between the freely doped atoms and the cluster impurity structure increases, the overall stability of the structure diminishes, consequently resulting in an elevation of the ionization energy. Analysis of the electronic density of states demonstrates that the electron orbit of the B atom makes negligible contributions to the impurity levels during P-B doping.

In recent times, the increasing concern for climate control has led to the widespread application of grid-connected inverter (GIC) based renewable energy systems. In addition, the increased usage of non-linear loads and electrification of the transport sector cause ineffective grid-frequency management and the introduction of harmonics. These grid conditions affect power quality and result in uncertainty and inaccuracy in monitoring and measurement. Incorrect measurement leads to overbilling/underbilling, ineffective demand and supply forecasts for the power system, and inefficient performance analysis. To address the outlined problem, a novel three-phase frequency component extraction and power measurement method based on Digital Lock-in Amplifier (DLIA) and Digital Lock-in Amplifier Frequency-Locked Loop (DLIA-FLL) is proposed to provide accurate measurement under the conditions of harmonics and frequency offset. A combined filter with a lowpass filter and notch filter for DLIA is employed to improve computation speed. A comparative study is performed to validate the performance of the proposed power measurement method, by comparing the proposed method to the windowed interpolated fast Fourier transform (WIFFT). The ZERA COM 3003 (a commercial high-accuracy power measurement instrument) is used as the reference instrument in the experiment

Background: Among the cognitive markers of Alzheimer’s disease (AD), semantic and phonemic verbal fluency tasks seem to be an early indicator of deterioration. The aims of this study are 1) to evaluate both types of Verbal Fluency in the early stages of AD in order to know which of them deteriorates earlier and, 2) to investigate if Verbal Fluency Tasks can help to differentiate between patients with Mild Cognitive Impairment (MCI) who will progress to AD two years later (progresses) and those who do not (non-progresses). Method: A Verbal Fluency Task was administered to 25 patients with MCI and their respective control subjects. All patients underwent a neuropsychological evaluation twice in order to follow up their global cognitive status. The second time, 8 of them converted to AD. Results: On the one hand, Phonemic Verbal Fluency deteriorates earlier than Semantic Verbal Fluency in MCI patients; on the second hand, although we found statistically significant differences between patients with MCI and AD in both type of fluency tasks, they were not found when comparing the performance of progresses and non-progresses. Conclusion: These results point to a greater impairment in Phonemic Verbal Fluency in MCI patients and its potential predictive capacity to predict conversion to AD.

A model for enabling the production of lower carbon cement paste is proposed in this research. The model is based on predicting the strength of cement pastes with partial replacement of wheat straw ash (WSA) and silica fume (SF), two major wastes of agricultural and industrial production. The model is set up based on response surface methodology and Box-Behnken design, and included a suite of mechanical tests with distinct replacements following a micro an meso characterization. The model shows capability for predicting strength of any cement paste, including partial substitution of WSA and/or SF at any combination level with excellent accuracy, which is verified with three validation mixtures demonstrating maximum errors of less than 6% at the three ages. The analysis of the response surface evidences that any cement replacement in the range of 0-20% WSA and above 5% SF allows the reduction of the carbon footprint by maximizing the incorporation of both wastes. The proposed model can be used by setting the required strength of cement paste and calculating the maximum possible replacement, which should contribute to making the construction industry more sustainable by utilizing local waste.

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

High-frequency circuit performance is significantly impacted by impedance variations, particularly within the low-resistance regime. Traditional Ohm’s Law based modeling approaches often fail to accurately predict circuit behavior in these conditions, leading to design inaccuracies and potential system failures. The Standard Ohm’s Law based model’s prediction of infinite current as resistance approaches zero is physically unrealistic and hinders its application in practical scenarios. Despite the recognition of these limitations, existing models have not comprehensively addressed the complex impedance behavior observed in high-frequency circuits. To overcome these challenges, this paper introduces a modified version of the Ohm’s Law incorporating an exponential correction term. The Modified Ohm’s Law’s accuracy was evaluated through simulated experiments across a wide frequency range (1kHz to 1GHz) using various electronic components. The findings demonstrate the superior performance of the modified model in predicting currents under low-resistance and high-current conditions compared to the Standard Ohm’s Law model. By providing finite and accurate current values, the proposed model effectively mitigates the unrealistic infinite current predictions of the standard approach. The enhanced predictive capabilities of the Modified Ohm’s Law hold significant implications for high-frequency circuit design and analysis. Its application can lead to improved performance and reliability in power electronics, telecommunications, and other high-frequency systems. By incorporating non-linear impedance behavior, the model offers a more accurate representation of real-world circuit conditions. Future research should focus on refining the exponential term’s parameters to optimize the model's accuracy across a broader range of applications. Additionally, real-time implementation and hardware validation are essential to assess the model's practical utility in complex circuit environments.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

Earthquake forecasting is arguably one of the most challenging tasks in Earth sciences owing to the high complexity of the earthquake process. Over the past 40 years, there has been a plethora of work on finding credible, consistent and accurate earthquake precursors. This paper is a cumulative survey on earthquake precursor research, arranged into two broad categories: electromagnetic precursors and radon precursors. In the first category, methods related to measuring electromagnetic radiation in a wide frequency range, i.e. from a few hz to several MHz, are presented. Precursors based on optical and radar imaging acquired by space borne sensors are also considered, in the broad sense, as electromagnetic. In the second category, concentration measurements of radon gas found in soil and air, or even in ground water after being dissolved, form the basis of radon activity precursors. Well-established mathematical techniques for analysing data derived from electromagnetic radiation and radon concentration measurements are also described with an emphasis on fractal methods. Finally, physical models of earthquake generation and propagation aiming at interpreting the foundation of the aforementioned seismic precursors, are investigated

This study presents the utilization of the Microwave InfraRed Thermography (MIRT) technique to identify and analyze the defects in the carbon-fiber-reinforced polymer (CFRP) composite rein-forcement of concrete specimens. At first, a set of the numerical models was created, comprising of the broadband pyramidal horn antenna and the analyzed specimen. The utilization the system operating at a power of 1000W in a continuous mode, operating at frequency of 2.45 GHz was analyzed. The specimen under examination comprised a compact concrete slab that was covered with an adhesive layer, thereafter, topped with a layer of CFRP. An air gap represented a defect at the interface between the concrete and CFRP within the adhesive layer. In the modelling stage the study investigated three separate scenarios: a sample with no defects, a sample with a defect located at the center, and a sample with a numerous additional random defects located at the rim of the CFRP matte, to analyze the effect of the natural reinforcement degradation in this area. The next phase of the study involved conducting experiments to confirm the results obtained from nu-merical modeling. In the experiments the concrete sample aged for 10 years with the defect in the center and naturally developed defects at the CFRP rim was used. The study employed numerical modeling to explore the phenomenon of microwave heating in complex structures. The aim was to assess the chosen antenna design and identify the most effective experimental setup. These con-clusions were subsequently confirmed through experimentation. The observations made during the heating process were particularly remarkable, since they deviated from earlier studies that solely conducted measurements of the sample post-heating phase. The findings demonstrate that MIRT has the capacity to be employed as a technique for detecting flaws in concrete structures reinforced with CFRP.

In order to study the effect of explosion load on the flexural performance of RC beams, two RC beams of the same size were carried out explosion test and bearing capacity test successively. Piezoelectric intelligent aggregate was embedded in the beams in advance to monitor the damage state of the beams, which realized the whole process of damage monitoring of the concrete inside the structure from explosion damage to compression damage.Based on wavelet packet analysis and residual bearing capacity damage evaluation criteria, the beam damage was quantitatively evaluated, and the bending bearing capacity of RC beam after explosion damage was compared and analyzed.The results show that the change of concrete strain gauge signal, the phenomenon of crack development and the increase of internal crack development index indicate that the RC beam is damaged by explosion load.After explosion damage, the damage index of the beam increases and the residual bending capacity decreases, which proves that the piezoelectric intelligent aggregate sensor can effectively monitor the bending capacity change of the component after explosion damage.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Mexico´s inhabitants have approximately 7 million dogs and cats as pets, of which there is no accurate information about their environmental impacts as a result of their feeding and comfort requirements. The objective of this study is to compare the environmental footprint between a dog and a cat in a family environment. For this purpose, a life cycle analysis was performed including, among other factors, its feeding and waste management in one year of life. Different environmental indicators including the carbon footprint were considered. It was found that the equivalent CO2 emission of a dog is twice that estimated for a domestic cat and that the main contribution is due to the food production. The ecological footprint that is generated when satisfying the requirements for pet´s well-being impacts in the environment contributes primarily to the carbon footprint.

A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

The effective utilization of medium-high temperature geothermal energy is pivotal in reducing carbon emissions and plays a crucial role in developing clean energy technologies. The MiDu ge-othermal field, situated in the southeastern region of Dali Prefecture, Yunnan Province, lies within the Mediterranean-Himalayan high-temperature geothermal belt and is characterized by abundant geothermal resources. However, due to its considerable depth, exploration poses significant risks, resulting in a total utilization rate of less than 0.5% of the total reserves. This study employs natural seismic data to perform a tomographic analysis of the geothermal system in the Midu basin. By examining the P-wave velocity (Vp) and velocity ratio of P-wave and S-wave (Vp/Vs) at various depths, the findings reveal that the basin comprises two distinct structural layers: the thrust basement of the Mesozoic and Paleozoic eras and the strike-slip extensional sedimentary layer of the Cenozoic era. A low-velocity anomaly in the central basin corresponds to the loose Cenozoic sedimentary layer. In contrast, high-velocity anomalies at the basin edges correlate with boundary faults and the Mesozoic-Paleozoic strata. Below a depth of 4 km, the Red River Fault and MiDu Fault continue to dominate the basin's structure, whereas the influence of the Malipo Fault dimin-ishes. The MiDu Fault exhibits higher thermal conductivity than the Yinjie Fault. It interfaces with multiple carbonate and basalt formations characterized by well-developed pores and fractures, making it a crucial conduit for water and a control point for geothermal storage. Consequently, the existence of high temperature geothermal resources for power generation should be concentrated around the Midu fault on the western side of the basin, while the Yinjie fault area is more favorable for advancements in heating and wellness.

Multiagent technologies give a new way to study and control complex systems. Local interactions between agents often lead to group synchronization also known as clusterization, which usually is a more rapid process in comparison with relatively slow changes in external environment. Usually, the goal of system control is defined by the behaviour of a system on long time intervals. When these time intervals are much longer than the time of cluster formation, clusters may be considered as new variables in a ``slow'' time model. We call such variables ``mesoscopic'' to emphasize their scale laying between the level of the whole system (macroscopic scale) and the level of individual agents (microscopic scale). Thus, it allows us to reduce significantly the dimensionality of a system by omitting considerations of each separated agent, so that we may hope to reduce the required amount of control inputs. Thus, we are often able to consider a system as a collection of ``flowing'' (morphing) clusters emerged form behaviour of a huge amount of individual agents. In this paper, we contrast such approach to the one where a system is considered as a network of elementary agents. We develop a mathematical framework for analysis of cluster flows in multiagent networks and use it to analyze the Kuramoto model as an attracting example of a complex networked system. In this model, a clusterization leads to sparse representation of dynamic trajectories in the whole quantized state space. With that in mind, compressive sensing allows to restore the trajectories in a high-dimensional discrete state space based on significantly lower amount of randomized integral mesoscopic observations. We propose a corresponding algorithm of quantized dynamic trajectory compression. It could allow us to efficiently transmit the state space data to a data center for further control synthesis. The theoretical results are illustrated for a simulated multiagent network with multiple clusters.

Climate change related increases in frequency and intensity of heatwaves affect viticulture, leading to losses in yield and grape quality. We assessed whether canopy architecture manipulation mitigates the effects of summer stress in a Mediterranean vineyard. Muscat of Alexandria grapevines were monitored during 2019-2020. Two canopy shoot positioning treatments were applied: Vertical shoot positioning (VSP); and Modulated shoot positioning (MSP), the west side upper foliage released to promote partial shoot leaning, shading the clusters. Clusters were sampled at pea size (PS), veraison (VER) and full maturation (FM). Measurements included rachis anatomy and hydraulic conductance, and aquaporins (AQP) and stress-related genes expression in cluster tissues. Results show significant seasonal and interannual differences in Kh and vascular anatomy. At VER, Krachis , Krachis+pedicels, and xylem diameter decreased but were unaffected by treatments. The Phloem/Xylem ratio was either increased (2019) or reduced (2020) in MSP, compared to VSP. Most AQP were down-regulated at FM in pedicels, and up-regulated at VER in pulp. A potential maturation shift in MSP was observed, confirmed by the up-regulation of several stress-related genes in all tissues. The study pinpoints the role of canopy architecture in berry water relations and stress response during ripening.

This research proposes a methodology for minimizing losses in transmission lines (LT), considering the reconfiguration of the architecture of the electrical power system (SEP). The implementation of this methodology redirects the power flow with optimal switching through its LTs to guarantee the stability of the voltage, angle, frequency, and power balance to minimize losses that affect the reliability and quality of the system. Optimal transmission switching (OTS) allows various types of analysis to be carried out, where the loadability of the lines, response times, and operating costs, among others, can be improved. This article proposes to minimize the losses in the transmission lines with the OTS by using AC power flows as a mixed integer nonlinear problem (MINLP). Several test scenarios evaluate the method’s effectiveness, determining the optimal topology for corrective control that optimizes power flows in different situations. It has been proven that this approach reduces losses compared to a base case by 99%, even in the face of N-1 or random contingencies, without losing load, maintaining the same active power dispatch, and finally verifying a strategic increase in dispatch of reactive power to maintain operating parameters within stable limits.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

Building a new-type power system is the key core of the green and low-carbon development of energy, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy-energy-environment (3E) system. This paper constructs a "Dual-Triangle" theoretical framework consisting of the “Energy Trilemma” and the "Sustainable Development Triangle", by combining historical statistical analysis with scenario predictive analysis, analyzes the historical characteristics of the "Dual Triangle" and future development trends under different scenarios, and studies the coupled interaction relationship of the "Dual-Triangle" to explore the optimal transformation path of the power system that is most beneficial to the coordinated development of the 3E system. The conclusions are as follows: (1) The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, the level of coordinated development of the 3E system fluctuated upwards, and the operation status of the "Dual Triangle" was stable. (2) Positive coupling relationship between "Dual Triangle" of scenario S1 is close, which can be regarded as the preferred path for the reconstruction of the power system. Due to phased development has clear development priorities, scenario S2 will artificially break the balance of the “Energy Trilemma”, which is a development model that dynamically mitigates the challenges of the “Energy Trilemma”.(3)The contradiction between economy and cleanliness is continuously weakened, and security has become the core contradiction of the "Impossible Triangle". The combination of renewable energy and energy storage is the key to resolving this contradiction. This paper conducts an integrated and innovative application of the "Dual-Triangle" theory, and the results provide a case basis for how to achieve the coupled interaction of the "Dual-Triangle", promote high-quality energy development, and support the sustainable development of the economy and society.

Background: We attempted to find if there were gender differences in different types of Negative life events and Suicidal ideation among Chinese adolescents, then analyze the relationship between different types of Negative life events and Suicidal ideation among these young students. Methods: Based on the data from 6 middle-schools and 3 universities in 3 cities of Western China, the gender difference in different types of Negative life events and Suicidal ideation and their related factors were investigated and analyzed in the study. Results: Gender differences were found during different types of Negative life events and Suicidal ideation; Negative life events could predict the intensity of Suicidal ideation by gender, to some specific types. Conclusions: Negative life events were proved to be risk factors of adolescents’ Suicidal ideation regardless of different gender stereotypes, but the specific classification of negative life events which had significant impact on adolescents’ Suicidal ideation also indicated significant gender divisions. For males, negative life events of punishment and adaptation had a significant and boosting impact on their Suicidal ideation, the higher the scores of punishment and adaptation negative life events had, the greater intensity of male adolescents were to have Suicidal ideation. Thus, the above two types of negative life events may be the main stressors predicting male adolescents' Suicidal ideation; For females, in addition to punishment, other types of negative life events all had significant impacts on their Suicidal ideation, which can be treated as the main stressors to trigger female adolescents' Suicidal ideation; Additionally, parents’ marital status of remarriage and divorce were proved to be significant indicators to adolescents’ Suicidal ideation, the age variable was proved to be strongly correlated with Suicidal ideation among female adolescents.

Dynamic time history analysis is a powerful and effective method to study the seismic response of tunnels, the dynamic time history to analyze the seismic response of the tunnel in the typical subway in this paper. The analysis results show that the overall levelling of the tunnel will not affect the tunnel too much, and the seismic response of the tunnel is mainly related to the relative displacement of the ground around the tunnel. The analysis results show that the internal force of the tunnel and the tunnel inclination have a good linear relationship, and the tunnel inclination can be used to measure the magnitude of the seismic response of the tunnel.

The fundamental period is one of the most important parameters for the design of new structures , as well as the estimation of the capacity of existing ones. However, its calculation requires the solution of an eigenvalue problem. Especially for new buildings, this can involve material and geometric parameters that are not known beforehand. Thus, to estimate it, various design codes and researchers have adopted a several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for the derivation of analytical equations for the fundamental period of Reinforced Concrete (RC) structures. The methodology is based on Machine Learning (ML) explainability techniques and specifically the so-called SHapley Additive exPlanations (SHAP) values. The novel methodology allows these equations to be constructed sequentially and in an informed manner, controlling the balance between accuracy and complexity. An extended dataset comprised of 4026 data points is employed. The results showed excellent accuracy, with a coefficient of determination R2≈0.95. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges.

Extreme climates such as droughts and hot extremes, may result in marked damage to crop yields and threaten regional and global food security. Maize is a major grain crop in Liaoning Province, severely affected by drought and hot events. The objective of this study is to advance an approach to estimate the risk of crop yield reduction under the compound dry-hot events (CDHE) in Liaoning Province. Results show that the ratio of stations experiencing compound dry-hot events during the twelfth leaf and maturity stage tended to increase on the time scale. On a spatial scale, the frequency of compound dry-hot events in central Liaoning Province was relatively low during the whole and various reproductive periods of maize. In addition to the twelfth leaf, western Liaoning Province is prone to compound dry-hot events. The high-risk area for the occurrence of the compound dry-hot events in maize is Chaoyang City, located in the western part of Liaoning Province, and the cities in the low-risk area account for about 64.3% of the total number of cities in Liaoning Province, mainly in the central and northern parts of Liaoning Province. Generally, this study emphasizes focusing on the impacts of compound dry-hot events on agricultural production and provides an index to assess the risk of compound dry-hot events on maize production.

This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve complex power flow equations. This study simulates a series of outage scenarios to evaluate the responsive-ness of the grid. The results obtained underline the crucial importance of reactive power management and highlight the urgent need to consolidate the grid infrastructure of North Cameroon. To increase grid resilience and stability, the paper recommends the strategic integration of renewables and the development of interconnections with other power grids. These measures are presented as viable solutions to meet cur-rent and future energy distribution challenges, ensuring a reliable and sustainable power supply for Cameroon.

The Glasgow Declaration called for scientifically based measurements of CO2 emissions in the tourism industry to achieve the goals of the Paris Agreement. Despite the economic and employment downturn caused by COVID-19, there are limited cases of environmental assessments related to tourism. This study estimates the carbon footprint of the tourism industry in major G20 countries before and after COVID-19. Understanding the characteristics of inbound tourism and domestic tourism is important as they impact different markets. GHG emissions from tourism mainly stem from transportation, but the type of travel and country differences also make souvenirs, accommodation, and food and beverages significant emission factors. The pandemic has greatly impacted the tourism industry. In 2020, GHG emissions from both domestic and inbound tourism significantly decreased, affecting the number of tourists. In some countries, measures against COVID-19 influenced these figures, and although signs of recovery were observed in 2021, the reduction rates varied by country. These emission reductions should be the goals pursued by the tourism industry in the post-COVID-19 era, requiring efforts to achieve sustainable tourism.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

Polyaromatic hydrocarbons (PAHs) are widespread in the interstellar medium (ISM). The abundance and relevance of PAHs call for a clear understanding of their formation mechanisms, which, to date, have not been completely deciphered. Of particular interest is the formation of benzene, the basic building block of PAHs. It has been shown that ionization of neutral clusters can lead to an intra-cluster ionic polymerization process that results in molecular growth. Ab initio molecular dynamics (AIMD) studies in clusters consisting of 3–6 units of acetylene modeling ionization events under ISM conditions have shown maximum aggregation of three acetylene molecules forming bonded C6H6+ species: the larger the number of acetylene molecules, the higher the production of C6H6+. These results lead to the question of whether clusters bigger than those studied thus far promote aggregation beyond three acetylene units, and whether larger clusters can result in higher C6H6+ production. In this study, we report results from AIMD simulations modeling the ionization of 10 and 20 acetylene clusters. The simulations show aggregation of up to four acetylene units producing bonded C8H8+. Interestingly, C8H8+ bicyclic species were identified, setting precedent for their astrochemical identification. Comparable reactivity rates were shown with 10 and 20 acetylene clusters.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

Ecological footprint calculators are digital tools that help individuals calculate their environmental or climate impact, with the aim of stimulating pro-environmental behaviour change. These footprint calculators typically take an information-provision approach, but this strategy assumes that increased levels of knowledge result in increased levels of pro-environmental behaviour (i.e., a reduced footprint). This is not a given – existing literature on the relationship between environmental knowledge and pro-environmental behaviour is inconclusive, and this relationship may be different from that of environmental knowledge and ecological footprint. As such, we investigated the relationship between environmental knowledge and ecological footprint as estimated by a footprint calculator. 448 Dutch participants completed an online survey, including an ecological footprint calculator. We found no evidence for a relationship between environmental knowledge and ecological footprint calculator outcome. Rather, an exploratory analysis of our data showed that environmental values were more important predictors of ecological footprint. The finding that increased levels of knowledge are not related to a reduced ecological footprint suggests that calculators would do well to move beyond information provision, and employ additional behaviour change strategies. Based on our exploratory analysis, we provide several concrete examples of potential strategies.

Fish kills in Southeast Asia are frequently associated with harmful algal blooms (HABs) and cause significant ecological and economic impacts. This paper serves as a review of fish kill events with focus on those related to HABs within the region. It examines the causative algal species, known mechanisms of fish mortality, and socio-economic consequences. Fish kills have been reported across multiple countries within Southeast Asia, with notable hotspots in the Philippines, Malaysia, Singapore, Indonesia, and Thailand. The common harmful microalgal species span a diverse group including dinoflagellates (Karenia spp., Karlodinium spp., Margalefidinium polykrikoides, Noctiluca scintillans), raphidophytes (Chattonella spp.), diatoms (Skeletonema spp. and Chaetoceros spp.), and cyanobacteria (Trichodesmium spp.). These microalgae lead to fish kills through mechanisms such as hypoxia, physical gill damage, and ichthyotoxin production. Freshwater fish kills linked to HABs have also been documented for the Philippines, but there is no or limited information for the region. Our review highlights the widespread and recurring nature of fish kills, their impact on fisheries and aquaculture, and challenges in managing and mitigating their effects. There are efforts at enhancing management and mitigation using clay and early-warning systems. However, it is essential to further improve monitoring efforts, the development and deployment of early-warning systems, and viable and holistic mitigation strategies to protect the region’s aquatic resources and dependent communities, especially as aquaculture and coastal development are increasing concurrent with a changing climate that can exacerbate the risks of fish kills and HABs in Southeast Asia.

The aim of the study was to assess the frequency of fish consumption in Poland including sea and freshwater fish species referring to some socio-economic determinants. The frequency of consumption was assessed using Food Propensity Questionnaire data from 1489 adults (722 males, 767 females) in 2019–2020. The fish consumption frequency in Polish adults was too rare compared to the recommendations (at least twice a week). About 80% of respondents consumed fish less frequently than recommended. Sea fish were mostly consumed less than once a month (31.8%) or 1-3 times a month (30.9%). The most of subjects (44.8%) consumed freshwater fish less than once a month. It was also depended on: educational level, economic status, place of residence, declared health status, and on BMI value or BMI status. However, taking into account sex, the occurrence of these relationships was different in men and women. It seems that educational activities and dissemination of proper nutrition principles is necessary in order to increase the consumption of fish in Poland. It should be important to emphasize health benefits of fish consumption in relation to possible adverse effects of their contamination.

This paper describes the proposal and development of a proof of concept (PoC) for a data logger system designed for operation and fault detection in bench testing of combustion engines. The main objective was to develop a data logger system that collects data on vibration, sound, temperature, and CO2 levels to identify ignition and combustion faults, thereby contributing to reducing greenhouse gas emissions (GHG). The paper presents (i) the proposal and development and (ii) the open-source code: proof of concept for a data logger system focused on detecting faults and monitoring the operational condition of benchtop combustion engines. The results indicate the feasibility of the PoC for data acquisition and dataset creation for Research and Development purposes.

The EXO+ hip protector was developed to minimize the risk of hip fractures in seniors following falls. This study evaluated its usability in a Singapore nursing home from the perspective of staff, focusing on Learnability, Efficiency, and Satisfaction. A mixed-method design was employed, with nursing home residents wearing the EXO+ daily for 8 weeks. Quantitative data on wear time was collected alongside qualitative insights from four repeated focus group discussions with care staff. In total, 15 residents (including 8 residents with cognitive impairments) and 9 staff participated. The analysis revealed six themes and fourteen subthemes related to EXO+’s usability. Residents wore the device for an average of 1.54 hours per day, with those having cognitive impairments showing increased wear times, especially in the mornings and on weekdays. Findings indicated that the EXO+ was learnable and satisfactory for nursing home use, provided there are adequate supporting features. The efficiency of the EXO+ was not fully measured since no falls were reported during the study period. The study offers valuable insights for potential design modifications and workflow adaptations to enhance the implementation of hip protectors in healthcare settings, highlighting facilitators and barriers to support device uptake.

In this research paper, design and development of pyramidal horn antenna for J-band application is reported. It is particularly designed for 17 dB gain and half beam width about 25 degrees at 6.93 GHz. Horn aperture, horn axial length and distance from the throat of the antenna to aperture are the main design constraints which are calculated and used for the antenna design and simulation. Beam width in E-plane and H-plane horn is calculated and it is 19.18 dB and 22.86 dB respectively. The reported antenna design shows good performance for J-band in radiometry, satellite, and radar applications.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

first_pagesettingsOrder Article ReprintsOpen AccessArticleThe Carbon Footprint of Spanish University Websitesby Sonia Sanchez-Cuadrado ¹ andJorge Morato ^2,*1Department of Library and Information Science, Complutense University of Madrid, 28010 Madrid, Spain²Department of Computer Science, Carlos III University of Madrid, Leganes, 28911 Madrid, Spain^*Author to whom correspondence should be addressed.Sustainability 2024, 16(13), 5670; https://doi.org/10.3390/su16135670 (registering DOI)Submission received: 31 May 2024 / Revised: 22 June 2024 / Accepted: 23 June 2024 / Published: 3 July 2024(This article belongs to the Special Issue Sustainable Education in the Digital Society: Applying Communication Technologies and Digital Tools to Education) Downloadkeyboard_arrow_down Browse Figures Versions NotesAbstractThe consumption of digital content is essential for economic and scientific development, leading to a high dependence on information and communication technologies, which significantly impacts the environment. Quantifying this ecological impact is challenging due to its intangible nature and the diversity of factors. This research focuses on assessing the energy consumption and CO₂ production of Spanish university websites to evaluate their levels of digital pollution and environmental impact. The calculation of the carbon footprint considers factors related to energy consumption and the use of green or renewable energies. The findings indicate that only 17% of university websites can be considered environmentally friendly. There are significant differences in CO₂ consumption and emissions depending on whether the websites implement environmentally sound practices. This study highlights the lack of initiatives aimed at reducing energy consumption and greenhouse gas emissions associated with web information management. The study shows that Spanish universities have the potential to contribute to the reduction in CO₂ emissions by implementing sustainable development practices on their websites. Furthermore, it fosters awareness about technology usage and its environmental impact, promoting the adoption of green and sustainable approaches.

Cisplatin is an election chemotherapeutic agent for many cancer treatments. Its cytotoxicity against neoplastic cells is mirrored by that taking place in healthy cells and tissues, resulting in serious adverse events. A very frequent one is ototoxicity, causing hearing loss which may permanently affect quality of life after successful oncologic treatments. Exacerbated oxidative stress is a main cytotoxic mechanism of cisplatin, including ototoxicity. Previous reports have shown antioxidant protection against cisplatin ototoxicity, but there is a lack of comparative studies on the otoprotectant activity and mechanism of antioxidant formulations. In this paper we report that a combination of vitamins A, C, E and Mg++ (ACEMg), previously shown to protect against noise-induced hearing loss, reverses auditory threshold shifts, promotes outer hair cell survival, and attenuates oxidative stress in the cochlea after cisplatin treatment, thus protecting against extreme cisplatin ototoxicity in the rat. The addition of 500 mg N-acetylcysteine (NAC), which administered individually also shows significant attenuation of cisplatin ototoxicity, to the ACEMg formulation, results in degradation of ACEMg otoprotection. Mg++ administered alone, as MgSO4, also prevents cisplatin ototoxicity, but in combination with 500 mg NAC, otoprotection also is greatly degraded. Increasing the dose of NAC to 1000 mg, also results in dramatic loss of otoprotection activity compared with 500 mg NAC. These findings support that single antioxidants or antioxidant combinations, particularly ACEMg in this experimental series, have significant otoprotection efficacy against cisplatin ototoxicity. However, an excess of combined antioxidants and/or elevated doses, above a yet to be defined “antioxidation threshold”, results in unrecoverable redox imbalance with loss of otoprotectant activity.

As in other parts of the world, natural disasters are increasingly causing significant loss of life and economic damage in Turkey. Natural disasters such as earthquakes, floods, storms, fires, and hailstorms have been occurring since ancient times, but now they are being compounded by factors such as climate change and the deterioration of natural environments. This has led to an increase in the frequency and severity of natural disasters and the resulting damage. It is important to finance the economic consequences of natural disasters that threaten individuals, commercial enterprises, and the country's administration. The frequency of the public's exposure to natural disasters, insurance awareness, the financial size of the disasters, the risk management policies of insurance companies, and the competence and willingness of the state and local governments to cover the damage are decisive factors. Prior to the increase in natural disasters, the country's administration was active and involved in first aid, rescue, and damage financing. However, it now has difficulty in financing the damage caused by natural disasters to individual, commercial, industrial, and agricultural assets and does not want to provide full assurance. This is because the dangers awaiting humanity in the future cannot be fully predicted. Private insurance companies undertake the task of taking over and distributing the risk in financing natural disaster losses and ensuring sustainability. Although this is a modern and rational solution, the problem is the insufficient number of insurance policies and insufficient premium production in countries where insurance awareness is not sufficiently developed. To increase premium production and geographical distribution of the risk, the state includes natural disaster insurances within the scope of compulsory insurances and mandates pre-disaster insurance policies. Following the earthquake disaster in the Marmara Region on 17.08.1999, compulsory insurance against earthquakes was introduced in Turkey with the Decree Law No. 587, which entered into force on 27.09.2000. This eliminated the obligation of the state to pay earthquake-related payments for the buildings determined by Turkish Natural Catastrophe Insurance Pool (TNCIP). Citizens have obtained modern, reliable, and regular insurance protection against earthquake damage. Naturally, the success or failure of Compulsory Earthquake Insurance (CEI) and Turkish Natural Catastrophe Insurance Pool (TNCIP) can be questioned after a major disaster. Since TNCIP is a new institution and other countries have not yet adopted it as an example, precedent institutions and past data cannot be included in the literature.

The ability of S. aureus to infect humans and cause a wide range of infections so efficiently is a consequence of the many variant virulence factors that it can produce. Superantigens (SAgs) make up a group of them and include staphylococcal enterotoxins (SE). The main objective of this study was to detect enterotoxin gene cluster (EGC) in S. aureus isolates that caused infections in Paraguayan children in the year 2017, through an observational and cross-sectional study. The detection of the EGC including the seg, sei, sem, sen, seo, and seu genes, was carried out by end-point PCR. 181 S. aureus isolates were collected, most of them caused skin and soft tissue infections (SSTI, n=140, 77%) and came from children under 6 years 50% (n=90). At least one SE gene was detected in 96% (174/181) of isolates and 24% (44/181) carried the entire EGC. An association between methicillin resistance and seg, sei, seo, and seu gene carriage (p ≤0.05) was observed. A high prevalence of EGC was detected in S. aureus isolates collected from infected children attending the main health care centers in Paraguay, its importance is due to the potential of these toxins to aggravate staphylococcal infections.

There have been exciting advances in our knowledge of primary glomerular diseases and nephrotic syndromes in recent years. Beyond the histological pattern from renal biopsy, more precise phenotyping of the diseases and the use of modern nephrogenetic helps to improve treatment decisions and sometimes also to avoid unnecessary exposure to potentially toxic immunosuppression. New Biomarkers led to easier and more accurate diagnoses and more targeted therapeutic decisions. The treatment landscape is getting wider with a pipeline of promising new therapeutic agents with more sophisticated approaches. This review focuses on all aspects of entities that are associated with nephrotic syndromes with updated information on recent advances in each field. This includes podocytopathies (Focal Segmental Glomerulosclerosis and Minimal-Change Disease), Membranous Nephropathy, Membranoproliferative Glomerulonephritis, IgA-Nephropathy, Fibrillary Glomerulonephritis, Amyloidosis and monoclonal gammopathy of renal significance in the context of the nephrotic syndrome, but also renal involvement in systemic diseases, diabetic nephropathy, and drugs that are associated with nephrotic syndromes.

Underreporting is the main limitation of the spontaneous reporting systems. This study aims to reduce underreporting enhancing public participation in vaccinovigilance through an SMS-based reporting system integrated with electronic immunization registries (IRs). Using the VigiVax software, parents of vaccinated children aged 2 years or younger received a single SMS inquiry about adverse events following immunization (AEFI). Responses were collected, validated by health operators and integrated with the information on IRs. AEFI reports were automatically submitted to the Italian Pharmacovigilance system. Among 254,160 SMS messages sent, corre-sponding to 451,656 administered doses (AD), 71,643 responses were collected (28.2% response rate), 21,231of them (8.3%) reporting an AEFI. After seriousness assessment based on clinical criteria, 50 reports (0,24%) were classified as serious. Among these, causality assessment identi-fied 31 reports at least potentially related to the vaccination (RR: 6.86/100,000 AD). Febrile seizures following MMRV vaccination accounted for 11 of these 31 cases, with an incidence of 32 per 100,000 AD. No fatal outcomes were reported. Our findings support the highly favorable risk profile of pediatric vaccinations and the possibility to improve spontaneous reporting through the integration of digital technologies.

Market turbulence with fiscal investment influences has altered IT infrastructure performance as business pursue extravagant new technology adoption. Yet, few studies have examined how shareware solution goes beyond Medium Size Enterprise that pushes efficiency and sustainability. This PLS-SEM integrated with dual primary compilation approach lessens shallow perceptions coupled with outlooks that streamline each phenomenal activity that is worthy of the necessity for competitive innovation. This unified model was applied to sampling respondents and analyzed using an ordinal regression relationship that generates a robust association that triggers the hypothesis acceptance. The adopting of BOINC shareware mesh network towards unified processing designs that were employed to build the yield by promising financial possibility using coordinated interworks hence improved group accomplishment and establishing greater esteem. This paper showcases a flexible inner IT infrastructure alongside the economic uncertainty with the framework advancement for Exostructure as a Service. Associated theoretical and practical implications were discussed.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damages. However, traditional methods such as smoke detection, visual analysis, and wireless signals pose unavailability in identifying fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying indoor fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire types and sound propagation, FireSonic quantifies the Heat Release Rate (HRR) of flames by analyzing the relationship between fire heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. Experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

This study presents an innovative approach for the utilization of industrial by-products and municipal waste in the production of sustainable and environmentally friendly cement mortar. We explored stabilized stainless-steel reduced slag (SSRS) and polyethylene (PE) plastic waste as partial replacements for aggregates. Various engineering properties of the resulting cement mortar specimens, including slump, slump flow, compressive strength, flexural strength, tensile strength, water absorption, and ultrasonic pulse velocity (UPV), were investigated through comprehensive experimental tests. The influence of different water-cement (w/c) ratios and substitution amounts on the engineering properties of the cement mortar samples was thoroughly examined. The findings revealed that an increase in PE substitution adversely affected the overall workability of the cement mortar mixtures, whereas an increase in SSRS amount contributed to enhanced workability. As for the hardened properties, a consistent trend was observed for both cases, with higher w/c ratios and substitution amounts leading to reduced mechanical properties. Water absorption and UPV test results validated the increased formation of porosity with higher w/c ratios and substitution amounts. This study proposes a promising method to effectively repurpose industrial by-products and municipal wastes, transforming them into sustainable construction and building materials. Additionally, a comparative analysis of transportation costs and carbon footprint emissions between SSRS-cement mortar and PE-cement mortar was conducted to assess their environmental impact and sustainability. Generally, higher w/c ratios and replacement levels corresponded to reduced carbon footprint. The geographical location of the source of SSRS and PE remains a challenge and studies to overcome this challenge must be further explored.

Emerging data processing techniques brought back into attention the HF range communication as an interesting alternative to third-party solutions for IoT applications, such as data transmission in distributed energy production facilities. The physical size of HF antennas, often comparable to the surrounding objects, require in-situ radiation measurements. Drone-borne measuring systems are already known as a flexible solution, but mostly restricted to higher frequency ranges. In this work, we propose to use an electrically small, folded dipole as a probe for drone-borne measurements on HF antennas. We also propose a calibration approach for the effects related to the near-field zone, and to the drone body proximity. The impact of ground reflection is also investigated. We show that despite its low, realized gain figure such a probe can provide stable results for near-field measurements, even at low input power levels.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

Footprints are powerful indicators for evaluating the impact of the bioeconomy of a country on environmental goods, domestically and abroad. In this study, we apply a hybrid approach combining a Multi-Regional Input-Output model and land use modelling to compute the agricultural land footprint (aLF). Furthermore, we added information on land-use change to the analysis and allocated land conversion to specific commodities. The German case study shows that the aLF abroad is larger by a factor of 2.5 to 3 than the aLF in Germany. In 2005 and 2010, conversion of natural and semi-natural land-cover types abroad allocated to Germany due to import increases was 2.5 times higher than the global average. Import increases to Germany slowed down in 2015 and 2020, reducing land conversion attributed to the German bioeconomy to the global average. The case study shows that the applied land footprint provides clear and meaningful information for policymakers and other stakeholders. The presented methodological approach can be applied to other countries and regions covered in the underlying database EXIOBASE. It can be adapted, also for an assessment of other ecosystem functions, such as water or soil fertility.

The destruction of natural habitat for cropland and pastures represents a major threat to global biodiversity. Despite widespread societal concern about biodiversity loss associated with food production, consumers access to quantitative estimates of the impact of crop production on the world’s species has been very limited compared to assessments of other environmental variables such as greenhouse gas emissions or water use. Here, we present a consistent dataset of the biodiversity footprints of pastures and 175 individual crops at country level. The data were generated by combining maps of the global distribution of agricultural areas in the year 2000 with spatially explicit estimates of the biodiversity loss associated with the conversion of natural habitat to farmland. Estimates were derived for three common alternative measures of biodiversity – species richness, threatened species richness, and range rarity – of the world’s mammals, birds, and amphibians. Our dataset provides important quantitative information for food consumers and policy makers, allowing them to take evidence-based decisions to reduce the biodiversity footprint of global food production.

One of the efficient data mining tools is density-based clustering, including the density grid-based clustering. However, a common drawback always existing in clusters made by the density grid-based method is the existence of weakly connected grids deriving mainly from noise. Appearing such an unwanted connection with a high frequency reduces the accuracy of the obtained cluster data space (CDS) and its application efficiency. Here, we present an essential improvement to overcome this problem. First, we describe a concept of the weak-connected grid cell (WCG) and present a fuzzy-type approximation to depict the density-based distribution of data points at grid nodes. Then, we propose a strategy of searching WCG for density grid-based clustering (SWCG-DGB) to set up a CDS, filter noise, and tune the created CDS. A buffer is deployed during this phase to collect border points and filter noise, which improves the computational time significantly, especially for noisy datasets. Results from numerical surveys reflected the compared efficiency of this method in clustering validity, including the accuracy of the number of clusters.

Weak signal spectral detection is a serious problem in many applications, especially the target possesses certain frequency band and under low signal-to-noise ratio (SNR). A kind of novel technique based on pre-whitening scale-transformation symmetric bistable parallel stochastic resonance (SR) is proposed to solve the problem. Firstly, the pre-whitening can ensure the even distribution of the receiving signal to fit the requirement of SR processing. Secondly, the scale- transformation can help to utilize the weak signal improvement property especially under low frequency band more effectively. Thirdly, the symmetric bistable parallel SR can try to reduce the variance of the clutter and additive noise fluently. Fourthly, by subtracting the steady state re-sponse of the selected symmetric bistable SR system from the parallel SR output, the weak signal spectral detection objective can be realized successfully. Simulation results based on the real sea clutter radar data guarantee the effectiveness and applicability of the proposed symmetric bistable PSR processing approach.

Background/Objectives: Monoclonal antibodies (mAbs) have revolutionized multiple myeloma (MM) treatment. However, post-marketing data on their neuropsychiatric safety is limited. This study aimed to evaluate neuropsychiatric adverse events (AEs) related to mAbs used for MM through a retrospective pharmacovigilance analysis using the Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database. Methods: Individual case safety reports (ICSRs) from 2015 to 2023 with at least one neuropsy-chiatric AE and one of the MM-approved mAbs as the suspect drug (i.e., daratumumab, elo-tuzumab, isatuximab, belantamab mafodotin, teclistamab, elranatamab, and talquentamab) were analyzed using descriptive and disproportionality approaches. Results: Unknown signals of disproportionate reporting (SDR) included cerebral infarction for daratumumab (n = 45; reporting odds ratio (ROR) = 2.39, 95% confidence interval (CI) = 1.79-3.21; information component (IC) = 1.54, IC025-IC075 = 1.05-1.9), elotuzumab (25; 7.61, 5.13-11.28; 3.03, 2.37-3.51), and isatuximab (10; 2.56, 1.38-4.76; 1.67, 0.59-2.4); mental status changes for daratumumab (40; 2.66, 1.95-3.63; 1.67, 1.14-2.04) and belantamab mafodotin (10; 4.23, 2.28-7.88; 2.3, 1.22-3.03); altered state of consciousness for daratumumab (32; 1.97, 1.39-2.78; 1.32, 0.73-1.74) and belantamab mafodotin (6; 2.35, 1.05-5.23; 1.6, 0.19-2.52); Guil-lain-Barre syndrome (GBS) for daratumumab (23; 6.42, 4.26-9.69; 2.81, 2.11-3.3), isatuximab (8; 10.72, 5.35-21.48; 3.57, 2.35-4.37), and elotuzumab (3; 4.74, 1.53-14.7; 2.59, 0.52-3.8); and orthos-tatic intolerance for daratumumab (10; 12.54, 6.71-23.43; 3.75, 2.67-4.48) and elontuzumab (4; 28.31, 10.58-75.73; 5 , 3.24-6.08). Conclusions: Our analysis highlighted several previously unacknowledged SDRs for MM-approved mAbs. Given the complex and not entirely understood etiology of some neuropsy-chiatric AEs, including GBS, further investigations are necessary.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

The landscape of clinical management for metastatic melanoma (MM) and other solid tumors has been modernized by the advent of immune checkpoint inhibitors (ICI), including programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors. While these agents demonstrate efficacy in suppressing tumor growth, they also lead to immune-related adverse events (irAEs), resulting in exacerbation of autoimmune diseases such as Rheumatoid arthritis (RA), Ulcerative colitis (UC), and Crohn’s disease (CD). The immune checkpoint inhibitors offer promising advancements in the treatment of melanoma and other cancers, but they also present significant challenges related to irAEs and autoimmune diseases. Ongoing research is crucial to better understand these challenges and develop strategies for mitigating adverse effects while maximizing therapeutic benefits. In this manuscript, we addressed this challenge using network-based approaches by constructing and analyzing molecular and signaling networks associated with tumor-immune cross-talk.. Our analysis revealed that IL6 is the key regulator responsible for irAEs during ICI therapies. Furthermore, we conducted an integrative network and molecular-level analysis, including virtual screening, of drug libraries, such as the Collection of Open Natural Products (COCONUT) and the Zinc15 FDA-approved library, to identify potential IL-6 inhibitors.. Subsequently, the compound amprenavir was identified as the best molecule that may disrupt essential interactions between IL6 and IL6R responsible for initiating signaling cascades underlying irAEs in ICI therapies.

Gestational diabetes mellitus (GDM) pathophysiology comprises clinical and genetic factors. In fact, GDM has been associated with several single nucleotide polymorphisms (SNPs). This study aimed to build a prediction model of GDM combining clinical and genetic risk factors. A total of 1588 pregnant women from the San Carlos Cohort participated in the present study, 1069 (67.3%) Caucasian (CAU) and 519 (32.7%) Latin American (LAT), 255 (16.1%) with GDM. The incidence of GDM was similar in both groups (16.1% CAU and 16.0% LAT). Genotyping was performed by IPLEX Mass ARRAY PCR selecting 110 SNPs based on literature references. The SNPs showing the strongest likelihood of GDM development were the rs10830963, rs7651090 and rs1371614 in CAU and rs1387153 and the rs9368222 in LAT. Clinical variables including age, pre-pregnancy body mass index and fasting plasma glucose (FPG) at 12 gestational weeks predicted the risk of GDM (AUC 0.648, 95%CI 0.601-0.695 in CAU; AUC 0.688, 95%CI 0.628-9.748 in LAT) and adding the SNPs modestly improved prediction (AUC 0.722, 95%CI 0.680-0.764 in CAU; AUC 0.769, 95%CI 0.711-0.826 in LAT). In conclusion, adding genetic variants enhanced the prediction model of GDM risk in CAU and LAT pregnant women. An RCT is underway to demonstrate its usefulness

The zinc oxide (ZnO) arrester is positioned on locomotive roof preventing other onboard electrical equipment from the harm caused by external and internal overvoltages. A number of locomotive arrester burnout and explosion accidents occur during traction supply network harmonic resonance causing train stoppages and power outages in the network. This study firstly carries out high-frequency tests to investigate the wide frequency band electrical characteristics of the locomotive arrester, explaining that the arrester is prone to fault during resonance. Consequently, a prognostics and health management (PHM) for locomotive arrester is proposed providing on online monitoring, fault warning and health assessment solutions, which is beneficial to the operation and maintenance of arresters in the future. The testing method and PHM scheme presented in this paper can be not only applied to the arrester, also readily expanded to other high-voltage electrical equipment on the electric locomotive.