Location-specific weather and climate information together with related advisory services are one of the crucial drivers of development in the 21^st century particularly in Sub-Saharan Africa. However, there still exist significant gaps in provision of weather and climate information at scales that effectively address the needs of local people for instance; local farmers and pastoralists. This therefore force local people to rely on indigenous knowledge (IK), to observe and forecast weather conditions. This situation does not exclude Uganda, and thus the current study assessed the efficacy of integrating indigenous knowledge systems into modern weather observational instruments in order to boost and act as a backup mechanism for modern weather observational instruments to increase accuracy and wide coverage of weather observations within Uganda. Results indicated that, respondents across the two pilot sites use a combination of plants, animals, insects, and human behaviours, meteorological and astrological indicators to observe and predict local prevailing weather conditions. Majority of the respondents particularly in Masaka district, believed that the use of indigenous knowledge to observe weather and climatic events is very reliable compared to their counterparts in Entebbe Municipality. Therefore, the integration of indigenous knowledge system in scientific weather observations is very vital.

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tool and the left-handed tool. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. For gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

China planned to promote the large-scale adoption of Electric Vehicles (EVs) in the thirteenth five-year plan, however, this target faces many obstacles. This paper was trying to analyze the main barriers to widespread adoption of EVs through a survey in Shenzhen, which has the biggest EVs market share in China major cities. Based on previous scholarly findings, this paper conducted a new study which collected 406 approved questionnaires among 500 participants. The study proposed five hypotheses to examine the main barriers to widespread adoption of EVs. The analysis conducted by statistical methods: two-way frequency tables, chi-square test, and factor analysis. The results noted that perception of EVs advantages and recharging access remained the main barriers to EVs large-scale penetration. Besides, financial incentives drop would not cause a significant decline of EVs adoption in future. The study proposed suggestions to carmaker and government policy administrator on the analysis and discussion.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

Cortex Periplocae, as a traditional Chinese herbal medicine, has been widely used for autoimmune diseases, especially rheumatoid arthritis. Due to its potential pharmaceutical values, more studies about the biological activities of Cortex Periplocae have been conducted recently. Meanwhile, the adverse reaction of Cortex Periplocae is not a negligible problem in clinic. In this article, we reviewed a series of articles and summarized the recent studies of Cortex Periplocae in the areas of phytochemistry and pharmacology. More than 100 constituents have been isolated and identified from Cortex Periplocae, including steroids, cardiac glycosides, terpenoids, and fatty acid compounds. The crude extracts of Cortex Periplocae and its active compounds exhibit various biological activities, such as cardiotonic effect, anticancer action, and anti-inflammatory effect. This paper aims to provide an overall review on the bioactive ingredients, pharmacological effect, and toxicity of this plant. Furthermore, this review suggests investigating and developing new clinical usages according to the above pharmacological effects.

Introduction. Autopsy is a medical procedure that consists of the corpse’s examination to determine the cause of death and obtain information on pathological conditions or injuries present in the body. In recent years there has been a reduction in hospital autopsies and an increase in forensic autopsies. Material and methods. A retrospective observational study was conducted on the reports of all the 645 hospital autopsies performed at Polyclinic of Bari from 2006 to 2021.Results. Group A, 2006-2009, 174 cases were studied: 58% male, 58% adults, 55% neonatology, pulmonary disease was the cause of death in 23%, and 55% of discrepancy between clinical and autopsy diagnosis. Group B, 2010-2013, comprised 119 cases: 52% male, 46% infants, 48% neonatology, pulmonary disease was the cause of death in 25%, and 56% of discrepancy between clinical and autopsy diagnosis. Group C, 2014-2017, comprised 168 cases: gender equality, 37% infants, 25% gynecology, pulmonary disease was the cause of death in 24%, and 58% of discrepancy between clinical and autopsy diagnosis. Group D, 2018-2021 comprised 184 cases: 56% male, 38% adult, 32% gynecology, pulmonary disease was the cause of death in 27%, and 58% discrepancy between clinical and autopsy diagnosis.Conclusion. The study of hospital autopsies at the Polyclinic of Bari shows that the discrepancy between clinical and autopsy diagnosis is around 56.75%. Thus, the hospital autopsy is helpful in the modern age, especially for diagnosing fetal and neonatal diseases.

Deployments of real-world object-detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, a conditioning method is investigated. The method aims to guide the training loop of thermal object detection systems by leveraging an auxiliary branch to predict the weather, while directly or indirectly conditioning the baseline detection system. Leveraging such an approach to train detection networks does not necessarily improve the performance of native architectures, however, it can be observed that conditioned networks manage to extract a signal from thermal images that guides the network to detect objects that baseline models miss. As the extracted signal appears to be quite noisy and very challenging to regress accurately, further work is needed to identify an ideal optimization vector.

This research presents the creation of a Virtual Reality experience for the Balzi Rossi museum, commemorating the centenary of Prince Albert I Grimaldi's archaeological work at the site. The project, a collaboration between the University of Genoa's 3D Lab Factory, Eurodrone, and the Balzi Rossi archaeological museum, aims to preserve and convey the site's heritage through advanced VR technology. Initially employing Unreal Engine 4, the team used photogrammetry for 3D reconstruction of the "Caviglione" and "Florestano" caves, notable for their ancient rock engravings. This first VR model, despite hardware limitations, provided an interactive exploration of the site. The subsequent development phase utilized Unreal Engine 5, incorporating Nanite technology for enhanced visual fidelity. This advancement resulted in a more detailed and immersive VR experience, presenting the Balzi Rossi cliff across different historical periods, including the Würm glaciation. Key to this phase was optimizing the VR experience for performance, focusing on stable frame rates and minimizing motion sickness, and integrating a multi-lingual interface for broader accessibility. Since November 2023, the VR setup at the Balzi Rossi Museum has been an educational and interactive feature, enabling visitors to explore the site's history virtually. This research demonstrates the potential of VR in cultural heritage preservation and education, providing a foundation for future technological improvements in the field.

Body-centered cubic bismuth (Bi) is considered an enticing pressure marker, and therefore it is highly desirable to command its accurate equation of state (EOS). However, significant discrepancies are noted among the previous experimental EOSs. In the present work, the EOS up to 300 GPa is theoretically obtained by direct solving the partition function via a direct integral approach (DIA). The calculated results nearly reproduce the hydrostatic experimental measurements below 75 GPa, and the deviations from the measurements gradually get larger with increasing pressure. Based on the ensemble theory of equilibrium state, DIA works with high precision particularly in high-pressure conditions, so the hydrostatic EOS presented in this work is expected to be a reliable pressure standard.

The understanding of weather and climate extremes provides academics, decision makers, international development agencies, nongovernmental organizations and civil society the necessary information for monitoring and giving early warning to prevent or minimize the risks associated with weather related hazards. Different researches were carried out to provide vital information that will further enhance the assessment of vulnerability and its impacts. Lack of proper understanding of weather and climate extremes was realized to be responsible for the huge and devastating losses that could have being averted or minimized over the past decades. Different countries and institutions have put in place a number of ways to increase sensitization and awareness of weather extremes. This became necessary in order to reduce the losses associated with these extremes both on local and regional scales.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. The solar wind O6+ ions which are the primary charge state for oxygen ions in solar wind are considered. The line emissivity of the charge-exchange born O5+ ions are calculated by Spectral Analysis System for Astrophysical and Lab oratory (SASAL). It is found that the emission lines from the O5+ ranges from 105.607 to 118.291 eV with the strong line at 107.047 eV. We then simulated the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm-2 s-1 sr-1 on the dayside, which is 4.5 times that before the solar wind structure reaches the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at north pole.

Background: The importance of knowing the workload of family nurses lies essentially in the possibility of improving health outcomes, care processes and the nurse’s professional life. There is a lack of studies that fully describe nursing workload in primary care, particularly, in the context of family health nursing, and the ideal metrics to be used remains unknown, making it impossible to characterize and therefor provide the necessary insight to acknowledge the different contributions of several aspects that embodies the global workload of family nurses. The objective of this scoping review was to map the known evidence and characterize the instruments used to assess the workload of family nurses. Methods: Scoping review, according the Joanna Briggs Institute proposed methodology for scoping reviews, consisting of 3 research stages: 1) initial research in Medline and CINHAL; 2) extended search, using keywords and search terms, in the following databases: JBI, CINAHL Complete, MEDLINE, Cochrane and Scopus; 3) Search the reference list of the selected articles. No time limit was defined. Results: Fourteen studies referring to ten as-sessment instruments were included. Nine of them analyze workload as a dimension of a broader instrument, and two studies refer to an instrument that focuses exclusively on workload. Con-clusions: The diversity of professional competencies and contexts, the conceptual complexity of workload and the absence of an theoretical framework, makes it difficult to identify consensual instruments to assess the workload of family nurses. This study was registered in Open Science Framework® with the following registration (https://osf.io/3k6vr).

Background: Actually, resonance frequency analysis (RFA) is the most extended method for measuring implant stability. The implant stability quotient (ISQ) is the measure obtained by the different RFA devices, however, inter- and intra- rater reliability and validity of some devices remains unknown. Methods: Thirty implants were placed in 3 different pig mandibles. ISQ was measured axial and parallel with Osstell® Beacon, Penguin® and MegaISQ® by 2 different operators and one operator performed a test-retest. Intraclass correlation coefficient was calculated to assess the intra- and inter-rater reliability. Pearson correlation coefficient was used to assess the validity. Results: The higher inter- and intra- rater reliability was obtained by Penguin® when measuring axial. The highest ISQ values were obtained using Penguin® in an axial measurement; the lowest, using the MegaISQ® in an axial measurement. The highest correlation values with the other devices were obtained by MegaISQ® measuring axially. Conclusion: Penguin® had a good reliability for measuring ISQ both inter- and intra- rater. Osstell® had good validity for measuring ISQ both axial and parallel and MegaISQ® had the best validity for measuring ISQ axial.

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.

Surgical navigation has evolved as a vital tool in maxillofacial surgery, offering precise and patient-specific data. This study explores the clinical applications and accuracy of intraoperative tool tracking in maxillofacial surgery. The research includes 37 patients with various pathologies who underwent surgery assisted by a surgical navigation system using tracked instruments. The study showcases two representative cases: one involving coronoid hyperplasia with mouth opening deficit and another addressing nose-orbital-ethmoidal-frontal ossifying fibroma resection. The results indicate that surgical navigation with tracked instruments provides high precision (<1.5 mm error), reduced surgical time, and less invasive approaches. The study highlights the potential for reproducible outcomes and increased safety, especially in complex cases. Despite some limitations, the synergy between surgical navigation and tracked instruments offers a promising approach in maxillofacial surgery, expanding its applications beyond current practices.

Until recently, the classic approach to mineral exploration studies is to bring the field samples/drill cores collected during field studies to the laboratory followed by laborious analysis procedures to generate the analytical data. This is very expensive, time consuming and difficult for exploring vast areas. But rapid technological advances in field portable analytical instruments such as portable ultraviolet–visible and near-infrared spectrophotometers, gamma ray spectrometer, pXRF, pXRD, pLIBS, and µRaman spectrometer have changed this scenario completely and increased their on-site applications in mineral exploration studies. These instruments are currently providing direct, rapid, on-site, real-time, non-destructive, cost-effective identification, and determination of target elements, indicator minerals and pathfinder elements in rock, soil, and sediment samples. These portable analytical instruments are currently helping to obtain accurate chemical and mineralogical information directly in field with minimal or no sample preparation, and providing decision-making support during field work as well as during drilling operations in several successful mineral exploration programs. In this article, the developments in these portable devices, and their contributions in the platinum group elements (PGE), rare earth elements (REE), gold, base metals, and lithium exploration studies both on land and on ocean bed have been summarized with examples.

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits - grain protein content (GPC) and thousand kernel weight (TKW) - across 179 Bulgarian contemporary and historic varieties, and landraces in three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, genotypic data from the 25K Infinium iSelect array, and a Bonferroni-adjusted p-value. As a result, 776 significant quantitative trait nucleotides (QTNs) organized into quantitative trait loci were discovered, including 25 loci associated with GPC, and 35 loci linked to TKW. Additionally, 14 environmentally stable QTNs were identified for TKW. Certain loci on chromosomes 2A, 3A, and 6B favorably in-fluenced GPC, while the locus on chromosome 6D impacted TKW positively. These loci may reflect novel associations to the two traits. Annotations for proteins involved in the remobilization of carbon and nitrogen from vegetative tissues to developing grain during senescence, as well as in the following buildup of resources required for seed germination, have been found for selected pu-tative candidate genes. These include genes coding for 11S globulin seed storage protein, glutamine synthetase, metalloendopeptidase, components of the ubiquitin/proteasome system, sucrose transporter, alpha-amylase, NAC and bZIP transcription factors. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

Sunflower is one of the oil crops and its planting areas was listed behind Soybean, peanut and rapeseeds in China. It will encounter different kind of diseases during planting, thus limited the seeds yield. Fusarium species are the major threat to different crops nowadays, it is very important to identify the species caused diseases on different hosts, so that the potential control measures may be explored. In Inner mongolia, Sunflower Disk Rot (SDR) was found in different sunflower planting field, the average infection ratio around 8.50%, typically in some verieties, its infection ratio reach to 11.67%. The diseased receptacle were collected from different locations and Koch’s postulate was conducted. The results showed that Fusarium verticillioides was the main causal agent caused SDR. Fungicide toxicity tests shown that Fludioxonil and Flutolanil were screened out from six fungicides for controlling F. verticillioides, with EC50 values of 0.05 µg/mL (R = 0.9825) and 0.96 µg/mL (R = 0.9964) , respectively. This is the first report of SDR caused by F. verticillioides, and it will alert sunflower reseachers to include SDR in the disease list, so as to control sunflower diseases with integrated management strategy successfully.

Even though the current classic aerospace propulsion methods are greatly improved, some of the most modern methods have already been introduced, among which the propulsion with electric motors. Energy can be obtained from hydrogen burners, honeycomb burners to protect the burning of hydrogen that burns 10 times faster than conventional oil fuels or alcohols. Hydrogen storage is also done in honeycomb cell reservoirs. Getting solar photovoltaic power even during flight is not yet profitable or efficient, but it can be proposed in the future for lighter and/or spacecraft. A great desire is to dismantle water in hydrogen and oxygen directly on the aircraft, so the stored fuel is actually water and the one actually used is the hydrogen removed from the water. In this case, there is no need for hydrogen storage. And other modern methods of propulsion and energy acquisition are briefly presented in the paper.

Abstract: Toxicity of emerging organic pollutants to photosystems of aquatic plants are still not well clarified. This study aimed to develop a novel ecotoxicological experimental protocol based on nanoscale electrochemical mapping of photosynthetic oxygen evolution of aquatic plants by SECM (scanning electrochemical microscopy). The protocol was also checked by confocal laser scanning microscopy (CLSM), the traditional Clark oxygen electrode method and chlorophyll fluorescence technique. The typical persistent organic pollutant 2,4-dichlorophenol (2,4-DCP) in water environment and the common aquatic Microsorium pteropus (M. pteropus) were chosen as the model organic pollutant and tested plant, respectively. It was found that the SECM method could well discriminated the responses of stoma micromorphology and spatial pattens of photosynthetic oxygen evolution on single stoma. The shape of stoma blurred with increasing 2,4-DCP concentration, which was in good agreement with the CLSM images. The dose-response curves and IC50 values obtained from the SECM data were verified by the data measured by the traditional Clark oxygen electrode method and chlorophyll fluorescence test. The IC50 value of single stoma oxygen emission of 24 h exposed plant leave, which was derived from the SECM current data (32535 μg L-1), was close to those calculated from the maximum photosynthetic efficiency (Fv/Fm) measured by chlorophyll fluorescence test (33963 μg L-1), and the Clark oxygen electrode method photosynthetic oxygen evolution rate (32375 μg L-1). The 72 h and 96 h 2,4-DCP exposure data further confirmed the reliability of the nanoscale stoma oxygen emission mapping methodology for ecotoxicological assessment. In this protocol, the procedures for how to collect effective electrochemical data and how to extract useful information from the single stoma oxygen emission pattern were well established. This study showed that SECM is a feasible and reliable ecotoxicological tool for evaluation of toxicity of organic pollutants to higher plants with unique nanoscale visualization advantage over the conventional methods.

Using two independent speckle imaging systems based on the recently published LSOCI method operating in contact mode on the skin, we assess the reproducibility of in vivo measurements and conduct an inter-instrument comparison. To this aim, we propose a calibration method to handle each imaging system as a comprehensive unit, which includes the laser source, optics, and camera. Our experimental results demonstrate that the microcirculation images produced by both instruments exhibit high accuracy and stability, with microcirculation activity values in excellent agreement, thereby paving the way for clinical applications.

The range of rating instruments in depression measurement and the depth of their analytical relevance constitutes a major development in this psychiatric and psychotherapeutic field of mental health. Though the competition is acute amongst these various instruments, the results for the public have been outstandingly positive. A depression rating scale is essentially a psychiatric measuring instrument utilized in the identification and ranking of depression severity within the patient. The scale provides the practitioner, psychiatrist or psychotherapist, with sufficient information to assess the severity of the depression plotted on the scale. Not used as a “diagnostic tool” itself, nevertheless, the depression rating scale does function as an effective device for designating and assigning a behavioral score which may, then, be used in establishing the severity of depression of value in the designation of a diagnosis and treatment formula. In this paper, we will take a close look at the leading depression rating scales and briefly summarize their scope of assessment value in rating depression.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

The significance of Kashmir conflict as one of the world’s longest running conflicts has frequently drawn the attention of both communication and international relations’ scholars which makes it an important issue from the perspective of interdisciplinary approach. The researchers have attempted to assemble a brief history of the Indian held Jammu and Kashmir Conflict from the time of partition in 1947 to the developments which took place till 2019 in the light of the available literature. The paper provides substantial historical context of the important events like instrument of accession, UN resolutions on Kashmir conflict, Simla Accord, Gawkadal Massacre , Lahore Declaration, Kargil War, killing of Burhan Wani, Uri attack and Article 370’s abrogation .

A regional modern river-sand geochemical mapping study in the majority of the Timor-Leste territory, including the research area, has not been previously reported. This study demonstrated the potential of modern river sand geochemical mapping as an effective method for lithological mapping, particularly in areas that face challenges in conducting efficient geological fieldwork and have limited geological information. A total of 53 modern river sand samples were collected and analyzed. Before examining the samples using wavelength-dispersive X-ray fluorescence for ten major elements, the loss-on-ignition method was employed. In the Clerec drainage basin, SiO2, CaO, and K2O showed positive correlations, and CaO was positively correlated with MnO. Similarly, in the Sahe River catchment, a positive relationship was observed between CaO, MnO, and TiO2. These findings suggested the presence of quartz, manganese, secondary K-bearing minerals, and secondary Ti-bearing minerals in carbonate rocks that have undergone silicification and other alteration processes. The principal component analysis results corroborated the SiO2/Al2O3 and K2O/Na2O ratios and the ICV findings. PC1 was related to the variations in Ca-poor and Ca-rich rocks; PC2 indicated notable inputs from SiO2-rich and SiO2-poor rocks; and PC3 revealed the possibility of contributions from secondary mineral assemblages related to silicification and low-grade metamorphic processes.

This review delves into the growing field of flexible and stretchable strain sensors utilized in human gait analysis, focusing on determining knee bending angle and muscle activities when a human subject walks. Recent advancements have enabled these sensors to accurately capture biomechanical parameters while accommodating the body's natural movements. We examine the principles underlying these sensors' design and fabrication techniques, emphasizing their flexibility, stretchability, and biocompatibility for seamless integration into wearable systems. Through critical evaluation of existing methodologies, we assess the reliability and validity of these sensors in quantifying gait parameters. Furthermore, we discuss this technology's clinical and research implications, highlighting its potential in personalized rehabilitation and sports performance optimization. We provide an overview of flexible strain sensors and their application for analyzing human walking gait, especially for observing walking gait in rehabilitation programs. First, we present the basic working principles of various flexible strain sensors. Second, by the application of flexible strain sensors as walking gait monitoring. Finally, current challenges and future opportunities in this research area are discussed. By synthesizing current knowledge and outlining future directions, this review aims to provide insights for researchers and practitioners seeking to leverage flexible and stretchable strain sensors for comprehensive human gait analysis.

We consider an alternative hypothesis of crater formation -- through an electric discharge machining (EDM) of the celestial bodies' surfaces. The source of the energy and the current itself may lie in the plasma infalling on a celestial body (from an extremely strong solar outburst or a nearby supernova). This mechanism may have been even more important than impact cratering in the early Solar System, with potential implications to the history of the dynamics of the System itself and solar activity in particular. In this light, the conclusion that shocked and metamorphosed minerals found within crater sites are evidence of hypervelocity impacts might be reconsidered. There are ambiguities that remain among sources of mineralogical formations. This study is ongoing with details of investigations supporting a new approach to crater formation not yet exhausted. Apart from mineralogical analysis and theoretical considerations regarding the large scale crater formation, we present a description of experimental studies of EDM acting at low pressures. Experimental results are made with crude setups and could easily be replicated on a larger scale with valuable research potential, as there is a well understood relation between the crater size and the energy needed for its production.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

We identified six natural "control knobs" for global weather and climate, which clearly regulate, increase and decrease global temperatures, thus performing a true temperature "control" on Earth. Identified control knobs act as Earth's orbital forcing. We present a detailed Earth orbital model, from where the analysis proceeds. Based on our orbital model, we compare meteorological data from all over the globe, in order to detect orbital forcing fingerprints. Ninety temperature graphs and bar charts demonstrate the results. Empirical meteorological data covers the Pacific ocean, the Atlantic ocean, Arctic and Antarctic sea ice extent, and presents land based meteorological measurements from all continents for the year 2020, as well as multi-decadal data from global daily temperature datasets. A supplementary file provides another 100 additional graphs of weather data and temperature charts. The global orbital "temperature control" is clearly observable in all graphs and charts. Until today, orbital forcing has not been integrated in weather models and CMIP6 climate models.It is concluded that the proposed new orbital forcing should be part of weather and climate forecasting models.

In every economy, Stock markets are part of the key elements the build it up. A few decades ago, there has been a significant change in Ghana stock market returns (GSE). Our study examines the statistical and economic significance of investor sentiment, based on weather conditions/changes, on stock market returns. OLS models, assisted by unit root tests were employed in analyzing the data obtained from the Ghana stock exchange platform from 2000 to 2017. From our literature review, we discovered that investors’ perceptions play a central role in finalizing the direction of stock market returns. Regarding our empirical results, we tested whether weather variations influence the investment decisions of investors; we discovered that temperature and cloud cover significantly influences stock market returns. This is because of mood changes is associated with weather conditions variations. However, sunshine per our regression coefficient shows a statistically insignificant impact on investors’ investment choices. Precipitation to a large extend influence stock market activities further affecting its results negatively as our regression results depicted. We concluded stock brokerage firms, companies, and investors (foreign/local) must incorporate weather changes/effects when strategizing about their investment outcomes.

In this paper, different cryptography algorithms were reviewed, and their relative merits were discussed. Symmetric and asymmetric cryptography algorithms were compared for time and space efficiencies. These experiments were run to measure the execution time and the memory footprints of various algorithms to understand the implications of their real-life applications. In the experiment performed, it was observed that elliptic curve cryptography was the most efficient in terms of encryption/decryption time, as well as memory usage.

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation at health systems and in industry.

This study compares the action of different handpieces (turbine, high-speed electric handpiece and piezoelectric device) on surface roughness of enamel and dentin when using diamond-coated working tips and burs of the same grit size. Experiment was conducted on 15 extracted first molars from patients aged 45 to 60 years. The occlusal portion was removed using a diamond-coated water wheel and then refined with a 120-130 μm grit bur in order to obtain a flat surface with an adequate exposition of the dentin core. Each surface was divided into three portions and every portion was finished with one of the three tested instruments. Rotary burs and piezoelectric tips had the same grit size (60 μm) and load on handpiece during preparation never exceeded 150 g. Surface roughness parameters (Ra, Rsk, Rku) were recorded with an optical profilometer and SEM analysis of treated surfaces and working tips was conducted. Ra and Rsk differed significantly between enamel and dentin only after using turbine (p=0.004 and p=0.007, respectively). No significant differences were observed in Ra, Rsk, and Rku between enamel and dentin when using high-speed electric handpiece or piezoelectric device. Turbine produced higher Ra and Rsk values on dentin than the other devices, while no significant differences were found between piezoelectric handpiece and high-speed electric contra-angle on both substrates. Summarizing, the findings of the present study demonstrated that turbine generated rougher surfaces on dentin compared to the other handpieces. Moreover, the turbine produced more asymmetrical surface profiles on both enamel and dentin. However, it should be considered that these differences in roughness (Ra) were within the range of 0.25-0.30 μm: it is still unclear if these variations, although statistically significant, will influence final clinical outcomes.

We continue to analyze basic constraints on human's decision making from the viewpoint of quantum measurement theory (QMT). As has been found, the conventional QMT based on the projection postulate cannot account for combination of the question order effect (QOE) and the response replicability effect (RRE). This was an alarm signal for quantum-like modeling of decision making. Recently, it was shown that this objection to quantum-like modeling can be removed on the basis of the general QMT based on quantum instruments. In the present paper we analyse the problem of combination of QOE, RRE, and the famous QQ-equality (QQE). This equality was derived by Busemeyer and Wang and it was shown (in the joint paper with Solloway and Shiffrin) that statistical data from many social opinion polls satisfies it. Now, we construct quantum instruments satisfying QOE, RRE, and QQE. The general features of our approach are formalized with postulates which generalize {\it Wang-Busemeyer} postulates for quantum-like modeling of decision making. Moreover, we show that our model closely reproduces the statistics of the famous Clinton-Gore Poll data with a prior belief state independent of the question order. This model successfully removes the order effect from the data to determine the genuine distribution of the opinions in the Poll. The paper also provides a psychologist-friendly introduction to the theory of quantum instruments - the most general mathematical framework for quantum measurements. We hope that this theory will attract attention of psychologists and will stimulate further applications.

The aim of this short communication is to find out the legal set up that complies with the environmental management in the textile sector of Bangladesh. This sector is the one of the biggest contributor to the economy of the nation. This sector contributes 81% to the total export earnings involving an immense number of stakeholders to its production processes. It is also true that this sector is largely responsible for the overall environmental pollution through its effluent discharge. The findings suggest that there are both international and national legal guidelines exist for environmental management in the said sector. In order to improve the environmental standards rehearsing lawful instruments is compulsory for the said industries.

The extensive collection of paper documents and books stored in the archives of universities worldwide is a hidden cultural heritage that is frequently inaccessible. To overcome this problem, the University of Genoa, Italy, seeks to collect, store, and digitize a wide variety of items, encompassing books, manuscripts, archival materials, and documents related to museum artifacts, which together form a cultural heritage of great importance and historical significance. To make such a cultural heritage accessible to both humans and machines, images and videos must be provided with alternate descriptions, metadata, and speech-to-text transcriptions while ancient texts, for which OCR techniques are often not effective, must be accompanied by word-for-word transcripts. This work presents the design of a transcription system for the “University Museum System” at the University of Genoa, Italy (SMA-UniGe), including user interface elements and users’ engagement techniques. The goal is to create an accessible digital heritage that can be enjoyed by all, facilitated by a community of digital volunteers who are eager to dedicate their time, have a great experience, socialize, and interact on the proposed transcription system. The system exploits gamification theory to transform the typically monotonous task of transcription into a captivating experience. This activity is in line with the so-called University third mission, i.e., the activity of public engagement that aims at generating knowledge outside the academic environment to the benefit of the social, cultural, and economic development.

Understanding and addressing dehumanization in healthcare is crucial due to its profound impact on patient care, ethical implications on patient dignity and autonomy, and its potential to affect the psychological well-being of healthcare professionals. The primary aim of this study was to develop reliable and valid instruments measuring two different types of dehumanization, namely animalistic dehumanization (i.e., stripping one of their uniquely human characteristics) and mechanistic dehumanization (i.e., striping one of their human nature characteristics) among healthcare professionals. In a cross-sectional validation study among healthcare professionals, we developed and tested measures of both animalistic and mechanistic dehumanization, focusing on dehumanization of patients (hetero-dehumanization) and oneself (self-dehumanization), respectively. All measures were developed and validated based on a concept analysis, a literature review, and an appraisal of pre-existing scales. The research was conducted among 400 nurses and medical doctors employed in Greek public hospitals. Coefficient Validity Ratio results showed that 100% of items were acceptable for both measures. The newly developed hetero-dehumanization scale encompassed two-factors (factor 1: animalistic dehumanization, factor 2: mechanistic dehumanization, Cronbach’s alpha was equal to 0.86 for each measure). The self-dehumanization scale was a mono-factorial measure mechanistic dehumanization (Cronbach’s alpha = 0.97). Two validated measures of (self and hetero-) animalistic and mechanistic dehumanization measures were developed for the assessment of dehumanization among health professionals, which will form the basis for future research in this important scientific field.

This paper studies pandemic viruses that spread during the period (1759–2020) according to solar activity cycles. Our findings and results include the following: (1) The severity of a pandemic correlates negatively with the strength of solar activity; (2) Pandemic viruses are classified into three types based on their compatibility with solar activity associations. Most of them spread through the quiet Sun, where viruses survive better in cold and rainy weather, and in stable geomagnetic fields without strong disturbances; (3) The emergence of new strains of influenza viruses was manifested in two ways. First, the annual epidemics due to antigenic drift. Second, pandemics recur every 1–12 solar cycles (about 11–120 years) due to viral reassortment of new subtypes, which results in antigenic shifts; (4) Pandemic viruses have two groups according to their recurring period: first, recurring in nine solar cycles; second, recurring in twelve solar cycles. Furthermore, we reassort pandemic viruses from their previous spread in the same periodic classification. Moreover, we derive a periodicity formula for each subtype of the pandemic virus as a spread date.

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

Living organisms with developed endocrine systems react in a complex way to environmental changes. Changing atmospheric pressure causes different blood pressures or hormone levels; carcinogenic radiation modifies the structure of DNA. Charged particles and ions act as neurotransmitters and block certain types of protein channels and receptors. A high concentration of carbon dioxide has an indirect effect on both blood pressure and neuron activity. The bioelectric nature of living tissues highlights the complexity of the connection between the dynamic physical environment and biological systems. Recent results from studies on the interactions and connections mentioned above are reviewed in this paper.

In the past two decades the world experienced an exceptional number of unprecedented extreme weather events, some causing major human suffering and economic damage, such as the March 2012 heat event, which was called “Meteorological March Madness.” From the beginning of space era a correlation of solar flares with pressure changes in atmosphere within 2–3 days or even less was reported. In this study we wanted to test the possible relation of highly warm weather events in North-East America with Solar Energetic Particle (SEP) events. For this reason we compared ground temperatures T_M in Madison, Wisconsin, with energetic particle fluxes P measured by the EPAM instrument onboard the ACE spacecraft. In particular, we elaborated case events and the results of a statistical study of the SEP events related with the largest (Dst ≤ −150nT) Coronal Mass Ejection (CME)-induced geomagnetic storms, between with the years 1997–2015. The most striking result of our statistical analysis is a very significant positive correlation between the highest temperature increase_. ΔT_M and the time duration of the temperature increase ΔT_M (r = 0.8, p <0.001) at “winter times” ( r = 0.5, p , 0.01 for the whole sample of 26 SEP examined events). The time response of T_M to P was found to be in general short (a few days), but in the case of March 2015, during a gradual P8 increase, a cross-correlation test indicated highest c.c. within 1 day (p < 0.05). The March 2012 “meteorological anomaly” was elaborated in the case of South-East Europe, where, beside a period of strong winds and rainfall (6-13.3.2012), intense precipitation in North-East Greece (Alexandroupoli) were found to be correlated with distinct high energy flux enhancements. A rough theoretical interpretation is discussed for the space—atmospheric extreme weather relationship we found. However, much work should be done to achieve early warning of space weather dependent extreme meteorological events. Such future advances in understanding the relationships between space weather and extreme atmospheric events would improve atmospheric models and help people’s safety, health and life.

Accurate seasonal weather forecasts are vital across a broad spectrum of applications, bearing significant environmental and socioeconomic implications. This importance renders the subject a matter of primary interest to a wide range of stakeholders, including the general public, agricultural sector, emergency responders, financial institutions, and policy strategists. The need for precision in long-term predictive models necessitates the development of innovative methodologies. These methodologies should be capable of deciphering atmospheric patterns and mechanisms with detail, especially at a local level, without the resource constraints that dynamical downscaling imposes. In response to this expanding demand, this study presents a novel solution, combining a stochastic deep learning methodology, specifically Generative Adversarial Networks (GANs), with a Digital Elevation Model (DEM). The cornerstone of the proposed model is the enhancement of gridded temperature fields from seasonal forecasts. The area of focus was the Hellenic region, wherein the spatial resolution is amplified from a coarse 1° x 1° grid to an impressively detailed 0.1° x 0.1° grid. This offers a transformative perspective for interpreting and employing this crucial meteorological data. The results suggest that the downscaled fields adequately approximate the actual spatial distribution, although the predicted values tend to slightly overestimate and in some cases underestimate the original ones. This study underlines the potential of this approach to significantly enhance the resolution and utility of weather forecasts, thereby contributing to a variety of sectors dependent on reliable meteorological data.

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

Information security plays a major role in the dynamics of today’s interconnected world. Despite the successful implementation and effectiveness of modern cryptographic techniques, their inherent limitations can be exploited by quantum computers. In this article we discuss Grover’s quantum searching algorithm and its impact on the security of modern symmetric ciphers. More specifically, we present its formal description and give an implementation of the algorithm using IBM’s Qiskit framework, which allows us to simulate and run the program on a real device. The developed program is modular and scalable, managing to properly run on any input state. We compare the performance of the algorithm running on the local simulator against its execution on a quantum processor from IBM’s lineup, noting the discrepancies between the results due to physical characteristics of the chip.

In this age of Facebook, Instagram, and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment is of interest to large numbers of organizations and people. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or it is more open and above board, such as researchers who want to make large groups of people aware of helpful innovations, what is at stake is often significant. In this paper, we review opinion dynamics including the extensions of many classical models as well as some new models that deepen understanding. For example, we look at models that track the evolution of an individual’s power, that include noise, and that feature sequentially dependent topics, to name a few. While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas. It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done.

: To improve the efficiency of rhizobial inoculation operations and address the current situation of rhizobial inoculation being labor-intensive, a spraying and control system for variable rate application (VRA) of liquid rhizobia was developed. The system can accurately regulate the spraying rate of liquid rhizobia according to the desired rate of rhizobia by incremental Proportional Integral Derivative (PID) closed-loop control algorithm, making it particularly suitable for precise spraying with small amounts. In the laboratory experiment of precise flow control, variation coefficient of spraying nozzle from 0.10 MP to 0.20 MP pressure were no more than 1.2%. A clear linear relationship was observed between the spraying rate and pressure. The maximum response time was 1.93 seconds, while the average response time was 1.62 seconds when controlling the spraying system for small amounts. Field trial was conducted in Heilongjiang Agricultural Xianghe Farm of China. Average seeds number and 100-seed weight of soybean plant with rhizobia inoculation treatment were better than control group, and average yield of spraying liquid rhizobia agent with full base fertilizer increased by 232.5 kg/hm2, representing a significant 7.4% improvement over the control group. The results of these trails clearly showed the positive effect of the spraying and control system for liquid rhizobial inoculation, which has superior to existing inoculation manners, and provides an important technical support for the widespread adoption of rhizobia technology.

The year 2022 was characterized by several major events that impacted the business world in Indonesia and even globally after the COVID-19 pandemic devastated all business sectors in Indonesia and the world. The current business environment is experiencing accelerating turbulence characterized by increasing intensity of business competition, rapid changes in the market, and industrial connections climate. The industrial connection climate of an institution can be measured by the pattern of relationships that occur between workers and management. Industrial connections are fundamental to the sustainability of an organization and are understood as an important factor in influencing institutional performance. This research aims to look at the impact of the existence of industrial connections instruments, turbulence in the business environment on the industrial connections climate. Besides, how the industrial connections climate relates to firm performance. The research design is an inferential quantitative, using sampling and snowball sampling methods with total sample are 406. The data collected were analyzed using Structural Equation Model using LISREL version 8.70. This research used a survey to build an SEM model that tested the effect of organizational turbulence and industrial connections instruments on organizational climate directly, and tested mediation on organizational performance. All related indicators have outer loading more than 0.5 and T-value more than 1.96. Therefore, the conclusion is that all measurements are valid. Composite Reliability (CR) and VE for all indicators are >0.7 and >0.5 respectively. This research shows that industrial assosiation climate has a positive effect on business turbulence and industrial connections instruments. However, industrial connections instruments show a greater impact on the assosiation climate. This is because industrial connections instruments are the media used in the implementation of industrial connections in organizations. This research also confirms that industrial assosiation climate is positively related to organizational performance. The result of the indirect influence (IE) of 0.20 with t value is 8.47 means that the climate of Industrial connections is able to mediate the influence of Industrial connections Facilities on Organizational Performance.

This paper reviews the current research on the speciation and determination of mercury by various analytical instruments such as, atomic absorption spectrometer (AAS), voltammeter, inductively coupled plasma optical emission spectrometer (ICP-OES), ICP-atomic emission spectrometer (AES), ICP-mass spectrometer (MS), atomic fluorescence spectrometer (AFS), spectrophotometer, spectrofluorometer, and high performance liquid chromatography (HPLC). Approximately 126 research papers on the speciation and determination of mercury by various analytical instruments published in international journals since 2015 are reviewed. All the analytical parameters, such as limit of detection, linearity range, quality assurance and quality control studies, applicability, and interfering ions studies evaluated in the reviewed articles are tabulated. This review find outs the lack of information on speciation studies of mercury in recent years. Another important prediction from this review is authors are not much studied about the concentrations of mercury in the atmosphere.

A 3-month (June-August) regime of the year 2002 summer rainfall (JJA2002) was simulated with 30 physics combinations using the Weather Research and Forecasting (WRF) model at 12-km horizontal grid resolution. The objective is to examine summer rainfall sensitivity to parameterization of microphysical, convective, and boundary layer processes and identify the best possible combination of parameterization options that perform relatively better in simulating the spatial and temporal distribution of summer rains over Ethiopia. The WRF simulated rainfall was evaluated against station data and satellite rainfall products (CHIRPS and ENACTS) using mean absolute error, Pearson and Pattern correlation coefficients (PCC), pattern correlation, and error in a number of rainy days as evaluation metrics. Summer rainfall is found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All simulations captured the spatial distribution of mean seasonal precipitation with PCC ranging from 0.89-0.94. However, all simulations overestimated precipitation amount and number of rainy days. Out of the 30, the simulations that use a combination of Grell-3D cumulus scheme, ACM2 boundary layer, Lin Microphysics, Dudhai shortwave radiation, and RRTM longwave radiation scheme ranked the top and provided the most realistic simulation in terms of amount and spatio-temporal distribution of summer rainfall.

Financial capacity instruments are psychometric tools designed to evaluate individual decisional capacity based on financial decisions. As tests are complex and need special conditions for administration and evaluation, it is difficult to use them in daily geriatric clinics. Our scoping review objective was to evaluate existing financial capacity instruments from the perspective of simplicity and portability. We evaluated one English speaking knowledge database (Medline) using a dedicated MeSH terminology. The review yielded one independent instrument, The Semi-Structured Clinical Interview for Financial Capacity, that can be easy adapted for every-day clinical use. It is simple to understand and perform and do not need trained personnel for administration. It can be finalized in 15 minutes. Initially validated on 261 subjects (with different forms of cognitive impairment), it showed good accuracy and precision mainly in subjects with cognitive impairment. The test is less apt to detect early or fluctuating cognitive impairment. Simplicity, the main advantage of the test, allows gamification fact that increases portability. Familiar images (coins, money) that are used for performing simple tasks does not need complex translation and adaptation. In form of a game, the test is suitable for serial administration, increasing the chance for early capacity reduction detection. Results reflect a physician judgement related to the subjects’ capacity to understand and execute simple financial instructions and not financial proficiency scores. The main limitation of our review is that we investigated only one, English speaking, knowledge database. The scoping strategy generated a financial capacity instrument that can be used in geriatric clinics for early diagnostic of decisional capacity reduction. Further studies are needed to evaluate the reliability and validity of the test in conditions of serial administration and in populations having various financial experience.

Stray light is a critical aspect for high performance optical instruments. When stray light control by design is insufficient to reach the performance requirement, correction by post-processing must be considered. This situation is encountered for example in the case of the Earth observation in-strument 3MI, whose stray light properties are complex due to the presence of many ghosts dis-tributed on the detector array. We implement an iterative correction method and discuss its con-vergence properties. Spatial and field binning can be employed to reduce the computation time but at the cost of a decreased performance. Interpolation of the stray light properties is required to achieve high performance correction. For that, two methods are proposed and tested. The first interpolate the stray light in the field domain while the second applies a scaling operation based on a local symmetry assumption. Ultimately, the scaling method is selected and a stray light reduction by a factor of 58 is obtained at 2σ (129 at 1σ) for an extended scene illumination.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

Our world is rife with challenges, many of which arise from both natural phenomena and human-made issues. While natural problems like diseases and environmental changes often elude our immediate control, a troubling number of today's issues are unintended consequences of human innovations. A striking example is the evolution of drug resistance. The discovery of antibiotics in the early days of modern medicine marked a monumental breakthrough, drastically reducing mortality rates and improving quality of life. Pioneers like Alexander Fleming, who discovered penicillin in 1928, ushered in a new era of medical treatment, transforming healthcare by effectively combating bacterial infections.However, the widespread and often indiscriminate use of these drugs has led to the emergence of drug-resistant pathogens. This natural phenomenon, accelerated by human practices such as overprescription and the misuse of antibiotics in agriculture, exemplifies a double-edged sword—initially celebrated solutions have now become significant challenges. The same drugs that once cured deadly infections are losing efficacy, making us vulnerable to diseases previously thought under control.The inventors of these drugs acted with the intent to save lives and alleviate suffering, unaware of the long-term consequences. Today, the responsibility to address drug resistance lies with us, armed with hindsight and a deeper understanding of microbial resistance complexities. This review explores the origins and evolution of drug resistance, the role of scientific knowledge, and the implications for future actions.

The challenge of campus design, like other aspects of contemporary environmental design, reveals a serious problem in education and practice. The foundational design theories of a century ago have been exposed as an obsolete way of thinking about cities, human nature, biological nature, and even the nature of mathematical and physical structures. Yet in practice, these discredited models persist, obscured by new theoretical language and extravagant “neoplastic” forms, but embodying persistent though untested ideologies and driven by systems inertia. This paper considers the campus design typology (including business campuses, commercial districts, hospitals, and schools) as a design paradigm for pedestrian public space with implications for human flourishing and well-being. We propose a specific human-oriented design method to encourage the well-being of occupants and improve the outcomes for creative development, education, and health.

This study provides an insightful overview of community-participatory governance and conflict management as instruments for utilizing resources for development in Sub-Saharan Africa (SSA). SSA is a region rich in natural resources but prone to conflict. The study is conceptual and critically engages the application of community governance and conflict management strategies that SSA countries need to adopt. The study praises the notion that community governance is characterized by recognizing and accepting intercultural diversity and creating a democratic institution for promoting good governance and conflict management in SSA. The study recommends that good community governance is required to drive local communities' development, growth, and transformation into a sturdy, innovative, and productive society capitalizing on opportunities. The study voice for the amalgamation of appropriate models and theories of governance cogently in the context of SSA due to the heterogeneity of communities. Therefore, to resolve conflict, conflict management, resolution, and transformation strategies are necessarily needed to accomplish a viable peace to maintain and sustain development.

Road traffic transportation has flourished in the process of urbanization by its advantages, but it also produces harmful environmental vibrations. The vibration problem is especially significant for production workshops with precision instruments. A field measurement was conducted to analyze the source characteristics and propagation laws of traffic-induced vibrations. Off the northern side of an urban highway lies a microelectronics workshop. When a large truck passes by, both the vibration responses on the ground and in the workshop considerably increase. Greater vibration is produced in the vertical direction than in the X and Y axes. The predominant frequency band of the vibration response caused by road traffic vehicles is lower than 20 Hz. The vertical vibration responses of the first and second floors in the workshop greatly exceed the VC-C limit because of the high daily traffic flow. Attribute to the excitation of the operation of the manufacturing equipment on the second floor, the vertical vibration level of the second floor is essentially higher than that of the first floor. Therefore, it is impossible to disregard the micro-vibration in the workplace produced by the manufacturing machinery as well as the ex-citement of the road traffic. The measured data can be used to test potential numerical models for forecasting vibrations caused by traffic on roads as well as to guide vibration assessment work throughout the planning and design stages of roads.

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect and analyze meteorological measurements. This paper presents the results of our analysis, demonstrating the effectiveness of citizen science in generating valuable environmental data. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. Our findings underscore the potential of citizen science to augment traditional monitoring efforts and inform decision-making processes in environmental research and management. The paper discusses the methodology employed, including data collection and statistical analysis techniques. Results highlight the reliability and accuracy of citizen-generated data, as well as the strengths of the MeteoTracker platform. The implications of our findings for environmental research, policy development, and public engagement are discussed, along with recommendations for enhancing the effectiveness of citizen science initiatives in environmental monitoring.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

With the rising cost of animal feed protein, finding affordable and effective substitutes is crucial. Walnut kernel cake, a polyphenols, fiber, protein and fat-rich byproduct of walnut oil extraction, has been underexplored as a potential protein replacement in pig feed. In this study, we found that feeding large Diqing Tibetan pigs walnut kernel cake promoted adipose deposition and improved pork quality during pig growth. Transcriptome analysis revealed the upregulation of genes ANGPTL8, CCNP, ETV4, and TRIB3, associated with adipose deposition. Pathway analysis highlighted enrichment in adipose-deposition-related pathways, including PPAR, Insulin, PI3K-Akt, Wnt, and MAPK signaling. Further analysis identified DEGs (differentially expressed genes) positively correlated with adipose-related traits, such as PER2 and PTGES. Single-cell transcriptome data pointed to the specific expression of CD248 and PTGES in adipocyte progenitor/stem cells (APSCs), pivotal for adipocyte differentiation and adipose deposition regulation. This study demonstrates walnut kernel cake's potential to substitute soybean cake in pig feed, providing high-quality protein and promoting adipose deposition. It offers insights into feed protein replacement, human functional food, fat metabolism, and related diseases, with marker genes and pathways supporting pig breeding and pork quality improvement.

I argue that the evidence of the Out-of-Africa hypothesis and the evidence of multiregional evolution of prehistorical humans can be understood if there has been interbreeding between Homo erectus, Homo neanderthalensis, and Homo sapiens at least during the preceding 700,000 years. These interbreedings require descendants who are capable of reproduction and therefore parents who belong to the same species. I suggest that a number of prehistorical humans who are at present regarded as belonging to different species belong in fact to one single species.

The demographic history of anatomically modern humans (AMH) involves multiple migration events, population extinctions and genetic adaptations. As genome-wide data from complete genome sequencing becomes increasingly abundant and available even from extinct hominins, new insights of the evolutionary history of our species are discovered. It is currently known that AMH introgressed with archaic hominins once they left the African continent. Current out of African human genomes carry fragments of archaic origin. This review focuses on the fitness consequences of archaic interbreeding in current human populations. We discuss new insights and challenges that researchers face when interpreting the potential impact of introgression on fitness and testing hypotheses about the role of selection within the context of health and disease.

The widespread proliferation of sensor nodes in the era of Internet of Things (IoT) coupled with increasing usage of wireless spectrums especially the ISM band makes it difficult to deploy real-life IoT. Currently, the cognitive radio technology enables sensor nodes communicate with each other through the licensed spectrum bands as well as the free ISM bands. Cognitive radio networks (CRSNs) are considered as a promising solution to the problem of spectrum under utilization and artificial radio spectrum scarcity. The paradigm of dynamic spectrum access allows secondary users (SUs) to utilize wireless spectrum resources which belong to primary users (PUs) with minimal interference to PUs. Due to the dynamic spectrum availability and quality, routing for SUs in multi-hop CRSNs is a challenge. In this paper, we introduce novel routing metrics that estimate both the future spectrum availability and the average transmission time with the consideration of both the global statistical spectrum usage and local instant spectrum resources. In our novel routing metrics, one retransmission is allowed and considered to reduce the probability of rerouting upon PU's arrival. Then, we propose two routing algorithms for multi-hop CRSNs. Finally, we conduct simulations, whose results show that our proposed algorithms lead to a significant performance improvement over the reference algorithm.

Reactive attachment disorder (RAD) and disinhibited social engagement disorder (DSED) manifest in individuals facing attachment system chal-lenges, particularly observed in minors under protective measures. The lack of standardized tools for assessing these difficulties and uncertainty about the most effective instruments from a psychometric perspective prompted this study. Using the COSMIN checklist, we systematically reviewed instruments assessing RAD, adhering to PRISMA and COSMIN guidelines. Examined tools included the Disturbance Attachment Inter-view, Preschool Age Psychiatric Assessment, Relationship Patterns Questionnaire, Assessment of RAD and DSED, Development and Well-Being Assessment, and Reactive Attachment Disorder Question-naire. Of the 11 articles analyzed, results highlight a research emphasis on internal consistency, structural and construct validity, sidelining other properties. While the instruments demonstrate strong structural validity, excellent internal consistency, and generally satisfactory error measurement results, the study concludes a dearth of investigations into psychometric properties for children and adolescents under protective measures.

A symbolic analysis of Archimedes’ periodical number system is developed, from which a natural link emerges with the modern positional number systems with zero. After the publication of Fibonacci’s Liber Abaci, the decimal Indo-Arabic positional system was the basis of the algorithmic and algebraic trend of modern mathematics, but even if zero plays a crucial role in algebra and mathematical analysis, zeroless positional systems show the same capability of producing efficient arithmetical algorithms based on operation tables over digits. The crucial role of digits is assessed, by considering a representation of numbers based on strings in lexicographic order. A new algorithm for the determination of decimal periods is presented,, by remarking the cruciality of this topic in number theory. Periods of ordinal numbers, and enumerations of recursive enumerability are shortly recalled. Concluding remarks are formulated about the deep relationship among numbers and information, which shed new light on a red line passing through the whole history of mathematics

Creating self-learning algorithms, developing deep neural networks and improving other methods that "learn" for various areas of human activity is the main goal of the theory of machine learning. It helps to replace the human with a machine, aiming to increase the quality of production. The theory of artificial neural networks, which already have replaced the humans in problems of detection of moving objects, recognition of images or sounds, time series prediction, big data analysis and numerical methods remains the most dispersed branch of the theory of machine learning. Certainly, for each area of human activity it is necessary to select appropriate neural network architectures, methods of data processing and some novel tools from applied mathematics. But the universal problem for all these neural networks with specific data is the achieving the highest accuracy in short time. Such problem can be resolved by increasing sizes of architectures and improving data preprocessing, where the accuracy rises with the training time. But there is a possibility to increase the accuracy without time growing, applying optimization methods. In this survey we demonstrate existing optimization algorithms of all types, which can be used in neural networks. There are presented modifications of basic optimization algorithms, such as stochastic gradient descent, adaptive moment estimation, Newton and quasi-Newton optimization methods. But the most recent optimization algorithms are related to information geometry, for Fisher-Rao and Bregman metrics. This approach in optimization extended the theory of classic neural networks to quantum and complex-valued neural networks, due to geometric and probabilistic tools. There are provided applications of all introduced optimization algorithms, what delighted many kinds of neural networks, which can be improved by including any advanced approaches in minimization of the loss function. Afterwards, we demonstrated ways of developing optimization algorithms in further researches, engaging neural networks with progressive architectures. Classical gradient based optimizers can be replaced by fractional order, bilevel and, even, gradient free optimization methods. There is a possibility to add such analogues in graph, spiking, complex-valued, quantum and wavelet neural networks. Besides the usual problems of image recognition, time series prediction, object detection, there are many are other tasks for modern theory of machine learning, such as solving problem of quantum computations, partial differential and integro-differential equations, stochastic processes and Brownian motion, making decisions and computer algebra.

Background: This research presents the use of photoplethsmography combined with Traditional Tibetan Pulse reading for the estimation of the three energies of a person: Activity, Transformation and Stability. The growing interest to revive traditional finger pulse reading attests of the need to find alternative ways to approach complex multi-source diseases as well as individualised diagnostic wearable or portable cost effective systems. Method: Our work is presented in two studies. The first study presents the development of the technique of photoplethsmography to classify the three energies. The second study presents a validation of this methodology on mental stress and relaxation. Results: Energies classification achieved a sensitivity above 85% and specificity above 72%. Mental stress and relaxation could be significantly discriminated from baseline condition. Harmonic analysis gave further insights into the dynamic of the pulse wave under stress/relaxation. Conclusion: The photoplethsmogram contains information pertaining to the mental and physiological state of a person as interpreted with the Eastern energies concepts. The implication of this work points towards a holistic understanding and impact of human activities, health and its environment.

Arabic was the language of philosophy, logic, Islamic sciences and rhetorical linguistic sciences. It was used in the writing of all sciences generally and became the language of world culture. Although the other languages of the Islamic world had a little share, but all peoples of Islamic countries; Arabs and non-Arabs, Muslims and non-Muslims attributed to that movement appreciated. Hence, Arab sciences flourished in their bright ages. Arabic language was a cultural container of the ancient Greek and Latin intellectual heritage. As the ancient Greek and Latin sciences and literatures were translated into Arabic in the bright ages of Islam. Arab language was a strong bond for them, as a common human heritage for the centuries to save it from being lost, as Muslims used it to develop their sciences and arts, as Muslims in the East used the Western sciences and literatures in the modern age, because they are not Eastern or Western, but the common efforts of all humanity. Arabic language opened for the dictionaries ​​and other tongues of cross-fertilization, civil friction and simulation. Arabic had become the language of sciences: mathematics, astronomy, engineering, logic, philosophy, mysticism, agriculture, industry and economics by the continuous efforts of scientists in their scientific research, invention, experimentation, knowledge collection and classification in various fields, where orientalists recognized the Arab leadership. That scientific and literary civilization moved to Europe through Italy, Andalusia, Crusades and the Ottoman Caliphate. Europeans learned Arabic language, literature and sciences in the centers of Toledo, Fez and cities of Maghreb.

In this paper, an extensive study on the operation, control and performance of a hybrid photovoltaic and wind turbine (PV-WT) system is presented. The study includes the operation of the hybrid systems under different weather conditions which are reflected on a change in the wind speed and irradiance. Various loading conditions are investigated such a change in active and reactive power demands for static and dynamic loads. The performance of the hybrid system is examined via MATLAB/SIMULINK simulations.

Weather radar plays a crucial role in the monitoring of severe convective weather. However, due to its limited detection range, it cannot conduct effective monitoring in remote offshore areas. Therefore, this paper utilized the Unet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibit significant diurnal cycles, but there are notable differences in their variation characteristics. To address this, we established 4 comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results show that adding the distribution map of the minimum cloud-top brightness temperature at the corresponding time in the model can effectively reduce the root mean square error (RMSE) of the model and enhance the accuracy of severe convective weather monitoring. This conclusion can be applied in the future to enhance and improve satellite quantitative precipitation estimation.

Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the insurance industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.

This paper presents a comprehensive simulation study of Automated Guided Vehicles (AGVs) in a fabricated production environment for toy car assembly. This paper uses Petri Nets for modeling and the General Petri Net Simulator (GPenSIM) for model implementation on MATLAB and simulation. The primary focus of this research is to analyze the operational efficiency of AGVs under varying conditions, particularly examining the impact of battery charge reduction rates and the strategic placement of charging stations within the production line. By employing Colored Petri Nets (CPN) within GPenSIM, we have developed a detailed and dynamic model that captures the intricate movements and interactions of AGVs in a simulated manufacturing setting. By presenting an approach to simulating AGVs in a production environment, this paper specifically focuses on two critical operational parameters: the battery charge reduction rate of AGVs and the number of charging stations available in the production line. Unlike previous studies that have primarily concentrated on general path planning or bottleneck detection in manufacturing systems, this paper delves into the intricate balance between AGV operational efficiency and the logistical constraints imposed by battery life and charging infrastructure.

There are many modern and traditional methods in valuing the performance of the company among them EVA, CVA are modern methods where as EPS, ROI, ROE etc. are some of the traditional methods. modern and traditional methods are not only used to check the performance of a company but it also used in investment decisions by the investors. Stern Stewart, managing partner of M/s Stern Stewart & Co. introduced a modified concept of economic profit in 1990 in the name of Economic Value Added (EVA) as measure of business performance and CVA is only a cash consideration in EVA. In this paper an attempt is made to study which method is used by the investors while taking the investment decisions i.e. traditional methods are modern methods. 100 respondents (Investors) are questioned and collected their views, we are assuming that there is no impact of traditional or modern methods while taking investment decisions by the investors.

The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~± 4°C) and salinity (~± 2.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on August 29th-30th during Hurricane Katrina in the middle of northern Gulf of Mexico, compared to an average MLD of ~20-40 m during pre-storm conditions. It took about 18-days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events.

This review aims to comprehensively synthesize the most recent research, collected from reputable databases, focusing specifically on transmission line. It examines advancements and challenges in data transmission. Fiber optics remains dominant, with new fiber designs and signal processing techniques like SDM improving capacity. Researchers are also addressing signal distortion and crosstalk. Beyond fiber optics, the review explores findings in coaxial cables and the promise of graphene for future high-speed transmission. Novel methods for mitigating crosstalk are presented. The findings highlight a trend towards more dependable, effective, and versatile transmission methods, with implications for various industries and electronic systems.

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

The research question in this article concerns how a competitive environment affects the learner (officer cadet's) personal leadership development and their relationship to their team and with future civilian foundations. More specifically, what are the possible learning effects of the “hidden” curriculum? This article investigates how a more than 250 years of leadership education provides new army officers with new skills and how such an environment may affect the cadets' leadership training. The paper builds on ethnographic data gathered during the three-year education program in most of the relevant practical locations and contexts. Findings regarding trust in their learning environment, cadets have reported scores of (Mean 2.83) on a 1 (low trust) to 5 (high trust) Likert scale, underpinning interviewdata regarding the lack trust in the academy and in their fellow cadets. Cadets also point out that competition has hindered their learning (Mean 2.50). These findings are interpreted in relation to possible negative effects stemming from internal competition and the evaluation system as a whole. The overall output of this system is a zero–sum game, and thus effects evaluative practices and learning processes. This study is of relevance to higher education officers responsible for designing learning environments.

Climate change related issues, such as yield decline, quality losses, accelerated phenology, extreme abiotic stress, pose growers new challenges in olive growing areas. Main cultural solutions that could alleviate stress and improve long-term sustainability of modern olive orchards are here briefly reviewed. Selecting varieties that are more resistant to drought or high temperatures would be the optimal solution to mitigate the effects of climate change, but the available scientific information is relatively scarce. Sustainability of irrigation implies that the less water is used, the better. Best results in terms of saving water and uniform application are obtained using micro-irrigation methods. Water can also be saved by not fully satisfying the tree water needs and different deficit irrigation strategies have been developed and tested for olive orchards. The use of green covers and the application of organic mulches and shredded pruning materials should be encouraged as these practices tend to stock carbon, increase organic matter content and water holding capacity of the soil. Olive orchards are efficient CO2 sinks as they can store considerable amounts of carbon in their organs and permanent structures. Using environmental friendly practices improves the ecosystem carbon budget and contributes to mitigate climate change.

The high incidence of psychopathologies recorded in today's human society, correlated with the high percentages of biodiversity loss, point to the need for an interdisciplinary approach of the scientific fields under study – Neurosciences and Biodiversity Conservation. Thus, our approach here pre-sents, in a synergistic manner, the significant correlation between mental health and the increased values of biodiversity in the ecosystems located in the immediate vicinity, especially those located in the middle of cities. Our approach aims to emphasize the importance of biodiversity conservation in the context of preserving mental health and general well-being. There are a series of recent ex-perimental demonstrations that outline the influence of natural elements on the human psyche and implicitly the effects of nature in the prevention and reduction of stress, anxiety and depression. And beyond the cognitive barriers of humanity in relating to the surrounding biodiversity must lie the desire to know the values of biodiversity and the absolute importance of its conservation. The sustainable relationship between man and living nature, seen as a complex of biodiversity, is dealt with by a branch of science called Human Ecology. Therefore, this study emphasizes the crucial need to know and respect the connection between man and nature, based, since time immemorial, on biophilia. And with the regression of ignorance and the correlated approach of several scientific fields, some at the intersection of humanities and natural sciences, one can observe the progress of preserving the dynamic balance within the ecosystems and implicitly the preservation of mental health and human well-being.

Among the biggest challenges facing the contemporary human condition, and therefore also education, is responding to the climate crisis. The source of the crisis is assumed to be absent-mindedness, presented by Leslie Dewart as a distortion of the development of human consciousness. Dewart's poorly-known philosophical consciousness study is presented in this paper in broad outline. The problems in the study of consciousness, the most important of which are the qualitative representations – qualia – and the question of free will, are also briefly discussed. These problems are then examined transcendental analytically, with the question of what one must assume in order to allow the emergence of these phenomena. From the resulting conception of causal relationship, we proceed to the circular causality as a prerequisite for life, namely the homeostatic systems and negative feedback. An organization of action that is essential to animals and humans is presented, using William Powers’ perceptual control theory (PCT), and the role of consciousness in this organization is drafted according to the studies of Martin Taylor. Action is seen as continuous problem solving, in which negative feedback is used to bring perceptions into line with the goals. The fundamental function of consciousness is revealed as the direction and enhancement of learning. Based on PCT, it can be shown that the main practical problems in animal and, especially, human action, are related to adverse side effects of action and the resulting various conflicts. The climate crisis is a typical example of the problematic side effects of collective action. Dewart's concept of absent-mindedness can therefore be defined as an inability to responsibly account for the side effects of action. Thus, the main task of education is to forestall, through negative feedback and in cooperation with learner consciousness, absent-mindedness and the problems it causes.

The paper investigates the issues of transliteration of Russian toponyms in a city environment and studies the efficiency of the Technical Reference Manual for the Standardization of Geographical Names published by the United Nations group of experts. The paper demonstrates the feasibility of the UN and Russian official transliteration standards and compares the suggested theoretical models with the practical applications found on the street signs, labels, maps, and other sources of navigation. The author presents a comprehensive account of practical transcription methods used in Moscow and offers a solution to a number of issues and discrepancies between popular transliteration models and their real-life application.

Following the coronavirus disease 2019 (COVID-19) pandemic, several studies have examined the possibility of correlating the virulence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, to the climatic conditions of the involved sites; however, inconclusive results have been generally obtained. Although either air temperature or humidity cannot be independently correlated with virus viability, a strong relationship between SARS-CoV-2 virulence and the specific enthalpy of moist air appears to exist, as confirmed by extensive data analysis. Given this framework, the present study involves a detailed investigation based on the first 20–30 days of the epidemic before public health interventions in 30 selected Italian provinces with rather different climates, here assumed as being representative of what happened in the country from North to South, of the relationship between COVID-19 distributions and the climatic conditions recorded at each site before the pandemic outbreak. Accordingly, a correlating equation between the incidence rate of the pandemic and the average specific enthalpy of atmospheric air was developed, and an enthalpy-based seasonal virulence risk scale was proposed as a tool to predict the potential danger of COVID-19 spread due to the persistence of weather conditions favorable to SARS-CoV-2 viability. For practical applications, a conclusive risk chart expressed in terms of coupled temperatures and relative humidity (RH) values was provided, showing that safer conditions occur in case of higher RH at the highest temperatures, and of lower RH at the lowest temperatures. The proposed risk scale was in agreement with the available infectivity data in the literature for a number of cities around the world.

An all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. The product is based on clear-sky LST retrieved from MSG/SEVIRI infrared (IR) measurements, complemented by LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a Microwave (MW) based LST product derived from AMSR-E measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

Deforestation is an important environmental problem in México and a key promoter of regional climate change through modifying the surface albedo. The objective of this research was to characterize the impact of deforestation and land use changes on surface albedo (α) and climate patterns in the Municipality of Tapalpa, Jalisco, México between the years 2014 and 2021. The main land cover types are coniferous forests (CF), oak and gallery woodlands (OGW), and annual agriculture (AA); which represent more than 88% of the regional territory (1066.03 km2). We used 2014 and 2021 Landsat 8 OLI images with topographic and atmospheric correction, in order to develop an inventory of albedo values for each land cover type in both time scenarios. Albedo images were generated by using the equation proposed by Liang in 2001, which is based on the reflectance of the bands 2, 3, 4, 5, and 7. Differences in albedo values were calculated between the years 2014 and 2021, and those differences were correlated with variations in climate parameters, for which we used climate data derived from the WRF model. In addition, the different land use changes found were classified in terms of triggers for increasing or decreasing surface albedo. Results showed that between 2014 and 2021, at least 38 events of land use change or deforestation occurred, with albedo increments between 1 and 11%, which triggered an average increment of 0.57% of the regional surface albedo in comparison to 2014 scenario. From 2014 to 2021, the albedo for CF, OGW, and AA, increased significantly (p&lt;0.001; Mann-Whitney U Test) by 79, 12, and 9%, respectively. In addition, the regional albedo increment was found to be significant and negatively correlated (p&lt;0.01 Spearman’s coefficient) with relative humidity (RH), maximum temperature (Tmax), minimum temperature (Tmin), and diurnal thermal range (DTR). The decrease in the regional values of relative humidity in 2021, occurred even when in that year the annual precipitation levels were higher than those of 2014, hence, reinforcing the fact of a possible climatic effect of the regional albedo changes. Conversion of CF and OGW into AA, perennial agriculture (PA), or grassland (GR) always yielded an albedo increment, whilst the conversion of AA to irrigation agriculture or PA triggered a decrease in albedo, and finally, the pass from GR or AA to protected agriculture caused albedo increase or decrease, depending on the greenhouse covers materials. Reducing deforestation of CF and OGW, conversion of AA or GR into PA, and selecting adequate greenhouse covers could help to mitigate regional climate change.

The paper presents an analysis of the concept of resilience within the context of a modern period example in Croatia, the Cvjetno housing estate in Zagreb, recognized as a cultural and historical complex. While extensive literature exists on the estate's architectural and historical significance, limited research explores its resilience. This study aims to fill this gap by examining Cvjetno estate's resilience to natural hazards, financial and economic challenges, and social crises. It investigates the estate's design, historical context, and physical characteristics through archival documents, historical records, and field research. The analysis focuses on the estate's planned resilience to natural disasters, particularly flooding from the nearby river Sava and seismic risks due to its location in a seismically active area. Drawing from the literature on resilient communities, the study draws on key characteristics and principles of resilience and explores how Cvjetno estate exemplifies these traits. The research contributes to a deeper understanding of resilience in urban planning and provides insights for enhancing resilience in similar historical settlements facing contemporary challenges.

The metaverse, a virtual world comprising a collective virtual shared space where users interact with one another through avatars and computer-generated objects, aims to closely mimic our real world by integrating elements of Artificial Intelligence (AI), immersive reality, advanced connectivity, and Web3. As metaverse technologies gain momentum across multiple sectors, including animal farming, their potential for addressing complex challenges such as climate change and sustainability in precision food production systems becomes increasingly apparent. However, it is crucial to consider the ethical implications and the role of sensor data and livestock behavior analysis in developing metaverse technologies for modern animal farming, given the sensitive and controversial nature of animal welfare. Failure to address these ethical considerations and harness the power of sensor data and behavior analysis could lead to a lack of credibility and insensitivity towards adopting metaverse technologies in the animal farming sector. It is essential to ensure that the development of metaverse technologies does not prioritize technology over animal welfare, ethics, socio-economic implications, and the potential for data-driven insights. Addressing diversity and equity in the context of animal farming and the metaverse is crucial to avoid perpetuating existing inequalities during the implementation of metaverse technologies. This groundbreaking paper ventures into unexplored territory, shedding light on the untapped potential of the metaverse for modern animal farming. While research on this topic is still in its infancy, we embark on a journey of visionary speculation, presenting a compelling technology forecast that envisions the extraordinary possibilities awaiting us in the future. By delving into the metaverse's transformative capabilities, we provide a glimpse into a world where animal farming transcends its traditional limitations and embraces a new era of efficiency, sustainability, and ethical practices.

In this article, we aim to show the capabilities, benefits, as well as restrictions, of three different air quality-related information sources, namely the Sentinel-5Precursor TROPOspheric Monitoring Instrument (TROPOMI) space-born observations, the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based measurements and the LOng Term Ozone Simulation – EURopean Operational Smog (LOTOS-EUROS) chemical transport modelling system simulations. The tropospheric NO2 concentrations between 2018 and 2021 are discussed as air quality indicators for the Greek cities of Thessaloniki and Ioannina. Each dataset was analysed in an autonomous manner and, without disregarding their differences, the common air quality picture that they provide is revealed. All three systems report a clear seasonal pattern, with high NO2 levels during winter-time and lower NO2 levels during summer-time, reflecting the importance of photochemistry in the abatement of this air pollutant. The spatial patterns of the NO2 load also showed, in both space-born observations and model simulations, the undeniable variability of the NO2 load within the urban agglomerations. The diurnal variability was furthermore clearly identified by the ground-based measurements, which also unquestionably revealed a Sunday minimum NO2 load effect, alongside the rest of the sources of air quality information. Within their individual strengths and limitations, the space-born observations, the ground-based measurements and the chemical transport modelling simulations, demonstrate unequivocally their ability to report on the air quality situation in urban locations.

With this article we would like to clarify the often-disregarded fact by virtue of which the European Missionaries in Asia acted as catalysts of a kind of nuanced acculturation named Accommodatio (adaptation). To a great extent they became harbingers of Culture and Science more than Faith itself to the dismay of many, including the Roman Church. Such cultural and scientific transference was actually two-pronged, for simultaneously they presented in Europe unique findings related to Language, e.g. the Chinese Characters (considered to be the sole natural language), Geography, Cosmology and even Governance. We would try to prove that such procedure contributed positively to the modern scientific notions of sustainability and to provide the kind of accoutrements that model the modern world as we know it.

Introduction: Industrial revolution 4.0 in the current era of globalization plays an active role in the modern era in Indonesia. The influence of increasingly advanced technology affects the millennial generation as the nation's next generation to make Pancasila the basis of the State or the foundation of a State. In addition, the millennial generation has begun to shift the guidelines of life from Pancasila so that the appreciation of Pancasila values is starting to disappear along with the development of the times and in line with the progress of Science and Technology (IPTEK). If this continues, then the values of Pancasila will be replaced by a new ideology that will cause behavioural changes in society. Purpose: this study aims to provide the proper application to millennial life using Pancasila values, knowing the behaviour of the millennial generation towards the implementation of Pancasila values, and making Pancasila a way of life. Methods: the method used in this research is Literature Review and takes sources from journals published in 2019 to 2021 related to the Pancasila theme and the millennial generation. Results: the study results show that with the existence of Pancasila as the ideology and principle of the State, it is expected to strengthen the attitude and nature of the community in accepting things outside the norms that exist in Indonesia. In addition, the younger generation is expected to be able to apply Pancasila values along with the development of science and technology in the modern era and current globalization. The research question is how to make the millennial generation implement Pancasila in life in the contemporary period and what solutions can continue to instil Pancasila values ​​in the 4.0 industrial revolution. Recommendation: the conclusion drawn is that Pancasila can become a unifying tool for the nation if the current generation or millennial generation still desires to maintain its values ​​, even though they are in a modern era and many influences from its outside cultures. Limitation: the limitation of this research is not getting all the data directly from the source. The researcher recommends using primary data for the following study to ensure its truth and accuracy better because the researcher directly obtains it.

Is there a wood-feeding insect inside a tree or wooden structure? We investigate several ways on how deep learning approaches can massively scan recordings of vibrations stemming from probed trees to infer their infestation state with wood-boring insects that feed and move inside wood. The recordings come from remotely controlled devices that sample the internal soundscape of trees on a 24/7 basis and wirelessly transmit brief recordings of the registered vibrations to a cloud server. We discuss the different sources of vibrations that can be picked up from trees in urban environments and how deep learning methods can focus on those originating from borers. Our goal is to match the problem of the accelerated—due to global trade and climate change— establishment of invasive xylophagus insects by increasing the capacity of inspection agencies. We aim at introducing permanent, cost-effective, automatic monitoring of trees based on deep learning techniques, in commodity entry point as well as in wild, urban and cultivated areas in order to effect large-scale, sustainable pest-risk analysis and management of wood boring insects such as those from the Cerambycidae family (longhorn beetles).

This study demonstrates the orientation and the ‘shape factor’ have pronounced effects on the development of the localized pressure fields inside of the helmet. We used anatomically accurate headform to evaluate four modern combat helmets under blast loading conditions in the shock tube. The Advanced Combat Helmet (ACH) is used to capture the effect of the orientation on pressure under the helmet. The three modern combat helmets: ECH, Ops-Core, and Airframe, were tested in frontal orientation to determine the effect of helmet geometry. Using the unhelmeted headform data as a reference, we characterized pressure distribution inside each helmet and identified pressure focal points. The nature of these localized “hot spots” is different than the elevated pressure in the parietal region of the headform under the helmet widely recognized as the under-wash effect also observed in our tests. It is the first experimental study which indicates that the helmet presence increased the pressure experienced by the eyes (as evidenced by the pressure sensors in the H8 and H9 locations), and the forehead (denoted as H1 location). Pressure fingerprinting using an array of sensors combined with the application of principle component analysis (PCA) helped elucidate the subtle differences between helmets.

Recently, NOAA has included GNSS (Global Navigation Satellite System) Radio Occultation (RO) data as one of the crucial long-term observables for weather and climate applications. To include more GNSS RO data in the numerical weather prediction system, the NOAA Commercial Weather Data Pilot program (CWDP) started to explore the commercial RO data available on the market. After two rounds of pilot studies, the CDWP decided to award the first Indefinite Delivery Indefinite Quantity (IDIQ) contract to GeoOptics and Spire Incs. in 2020. This study examines the quality of Spire data products for weather and climate applications. Spire RO data are collected from commercial CubeSats through careful comparison with the data from Formosa Satellite Mission 7–Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), ERA-5, and high-quality radiosonde data. The results demonstrated that although with lower Signal-Noise-Ratio (SNR) in general, the pattern of the lowest penetration height for Spire is similar to those for COSMIC-2. The Spire and COSMIC-2 penetrate heights are between 0.6 and 0.8 km altitude at the tropical oceans. Although using different GNSS RO receivers, the precision of Spire STRASP receivers is of the same quality as those of COSMIC-2 Global Positioning System - GPS, GALILEO, and GLObal NAvigation Satellite System – GLONASS (TGRS) receivers. The retrieval accuracy from Spire is very compatible with those from COSMIC-2. We validated Spire temperature and water vapor profiles by comparing them with collocated radiosonde data. Generally, over the height region between 8 km and 16.5 km, the Spire temperature profiles match those from RS41 RAOB very well with temperature biases &lt; 0.02 K. Over the height range from 17.8 to 26.4 km, the temperature differences are ~-0.034 K with RS41 RAOB being warmer. We also estimated the error covariance matrix for Spire, COSMIC-2, and KOMPSAT-5. Results showed that the COSMIC-2 estimated error covariance values are slightly more significant over the oceans at the mid-latitudes (45oN-30oN and 30oS-45oS), which may also be owing to COSMIC-2 SNR being lower at those latitudinal zones.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

Physiological storage disorders continue to cause sizable economic losses in a range of commercially important pomefruit cultivars. Given similar storage regimes, the incidence and severity of browning disorders in the apple cultivar ‘Braeburn’ can vary in different years in a way that can be explained by the interaction of preharvest seasonal and orchard factors. Over a three-year period (2016 to 2019) at the Kompetenzzentrum Obstbau-Bodensee (KOB) in Southwest Germany a range of orchard and storage treatments were conducted for: air temperature during cell division for three weeks post petalfall or during four weeks preharvest, calcium orchard sprays, crop load and harvest timings. Following controlled atmosphere (CA) storage, the disorder incidence for internal browning and cavity formation varied markedly over the three different growing seasons. Crop load treatments strongly influenced the expression of browning disorders in all years. Differences in air temperatures (△ +/- 2 °C compared to ambient) during the cell division period showed little effect on browning incidence. Warm night temperatures (>10 °C) prior to harvest can reduce internal browning in ‘Braeburn’ apples during CA storage and shelf-life.