Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

Background: This study aimed to identify the association between occupational stress and depression-well-being by proposing a comprehensive and flexible job burden-capital model with its corresponding hypotheses. Methods: For this research, 1618 valid samples were gathered from the electronic manufacturing service industry in Hunan Province, China; self-rated questionnaires were administered to participants for data collection after obtaining their written consent. The proposed model was fitted and tested through structural equation model analysis. Results: Single-factor correlation analysis results indicated that coefficients between all items and dimensions had statistical significance. The final model demonstrated satisfactory global goodness of fit (CMIN/DF=5.37, AGFI=0.915, NNFI=0.945, IFI=0.952, RMSEA=0.052). Both the measurement and structural models showed acceptable path loadings. Job burden and capital were directly associated with depression and well-being or indirectly related to them through personality. Multi-group structural equation model analyses indicated general applicability of the proposed model to basic features of such a population. Gender, marriage and education led to differences in the relation between occupational stress and health outcomes. Conclusions: The job burden-capital model of occupational stress-depression and well-being was found to be more systematic and comprehensive than previous models.

Background: Rhizarthrosis is the most frequent form of osteoarthritis and is responsible for pain, disability and reduced functionality. Recent studies support the possibility of providing Extracorporeal Shock Wave Treatment in patients suffering from osteoarthritis. Shock wave biostimulation may be able to modulate joint inflammation and stimulate chondorocytes in a reparative sense. The aim of the study is to investigate the clinical effects of shock wave treatment in patients suffering from arthritis of the first finger. Methods: A prospective clinical study was designed, in which seventy-two patients suffering from rhizoarthrosis were randomized to treatment with shock waves or exercises; in both groups an immobilization brace of the first finger was used. At recruitment and at 1, 3 and 6 months, patients received assessments of pain (VAS), functionality (FIHOA), disability (DASH); furthermore, the perception of improvement was monitored during follow-ups (Roles and Maudsley Score). Results: In both groups there was a significant improvement in all scores in the comparison between recruitment and 6 months; this improvement was confirmed for VAS, FIHOA and DASH in the comparison between individual time points in both groups, except for the comparison between 3 and 6 months for all scales in the exercise group and for FIHOA in both groups. The perception of improvement was statistically better in the shock wave group than in the exercise group at 1, 3 and 6 months. The regression analysis showed that the reduction of VAS and DASH are statistically influenced by shock wave treatment (p<0.001). Conclusions: Both shock waves and exercises, together with the use of the brace, are effective in managing pain, functionality and disability in patients suffering from arthritis of the first finger. The shock wave treatment ensured further improvement at 6 months

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that provides information on the microstructural organization of biological tissue. dMRI allows for non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, at 7T wavelength effects cause lower transmit magnetic field efficiency in the human brain, mainly in the posterior fossa region, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. Here we demonstrate the feasibility and effectiveness of using this RF enhancer for in-vivo dMRI at 7T. The use of the RF enhancer can effectively recover signal dropouts in the regions with intrinsically lower SNR such as cerebellum, leading to a better depiction of principal fiber orientations and enhanced visualization of extended tracts to the brainstem and pons enabling more comprehensive delineation of the full white matter architecture.

Aerobic vaginitis (AV) is a distinct clinical entity characterized by inflammation and abnormal vaginal microflora. Often mistaken for bacterial vaginosis, AV remains relatively unknown and underdiagnosed. AV's understanding is evolving, with some experts suggesting it may primarily be an immunological disorder, the prevalence of which ranges from 7-13% in non-pregnant women and 4.1-8.3% during pregnancy. Pregnancy can affect susceptibility to vaginal infections, leading to adverse outcomes for the woman and the newborn. This review summarizes the correlation between AV and adverse pregnancy outcomes, particularly preterm birth, the leading cause of morbidity and mortality among neonates. Improved understanding of AV's impact on pregnancy outcomes can lead to early recognition, proper management, and effective interventions. While some studies support an association between AV and preterm labor, the existing knowledge of this relationship remains limited. The evidence suggests that AV may contribute to adverse pregnancy outcomes, mainly preterm birth, but further research is needed to establish a definitive link. Further studies are needed to investigate the underlying mechanisms and clarify AV's role in premature labor. A comprehensive understanding of AV's impact on pregnancy outcomes is crucial for early recognition, appropriate management, and effective interventions.

Abstract: Background/Objectives: There are indications that the microbial composition of the maternal mucosal surfaces is associated with adverse events during pregnancy. The aim of this review is to investigate the link between vaginal microbiome alterations and gestational com-plications risk. Methods: This comprehensive literature review was performed using Medline and Scopus databases. The following search algorithm was used "Pregnancy Complications"[Mesh] AND (Vagin*) and after literature screening 44 studies were included in the final review. Results: The studies that were included investigated the association between vaginal microbial composi-tion and preterm birth, miscarriage, preeclampsia, ectopic pregnancy, gestational diabetes mellitus, chorioamnionitis and preterm premature rupture of membranes. In most of the studies, it was well established that increased microbial diversity is associated with these conditions. Also, the depletion of Lactobacillus species is linked to most of the gestational complication, while the increased relative abundance and especially L. crispatus may exerts a protective effect in favor of the pregnant woman. Several pathogenic taxa including Gardnerella, Prevotella, Sneathia, Bac-terial Vaginosis-Associated Bacteria-2, Atopobium and Megasphera seem to be correlated to higher maternal morbidity. Conclusions: Vaginal microbiome aberrations seem to have an as-sociation with pregnancy related adverse events, but more high-quality homogenous studies are necessary to reliably verify this link.

Functional articular cartilage regeneration remains an unmet medical challenge, increasing the interest for innovative biomaterial-based tissue engineering (TE) strategies. Hydrogels, 3D macromolecular networks with hydrophilic groups, present articular cartilage-like features such as high water content and load-bearing capacity. In this study, 3D porous polyethylene glycol diacrylate (PEGDA) hydrogels were fabricated combining the gas foaming technique and an UV-based crosslinking strategy. The 3D porous PEGDA hydrogels were characterized in terms of their physical, structural and mechanical properties. Our results showed that the size of the hydrogels pores can be modulated by varying the initiator concentration. In vitro cytotoxicity tests showed that 3D porous PEGDA hydrogels presented high biocompatibility both with human chondrocytes and osteoblast-like cells. Importantly, the 3D porous PEGDA hydrogels supported the viability and chondrogenic differentiation of human bone marrow-derived mesenchymal stem/stromal cells (hBM-MSCs)-based spheroids as demonstrated by the positive staining of typical cartilage extracellular matrix (ECM) (glycosaminoglycans (GAGs)) and upregulation of chondrogenesis marker genes. Overall, the produced 3D porous PEGDA hydrogels presented cartilage-like mechanical properties and supported MSC-spheroid chondrogenesis, highlighting their potential as suitable scaffolds for cartilage TE or disease modelling strategies.

Background/Objectives: Radical cystectomy (RC) represents one of the most complex and morbid surgical procedures in Urology. Extraperitoneal open RC has emerged as an alternative to traditional transperitoneal approach for the treatment of muscle-invasive bladder cancer. Frailty is one of the most important risk factors for perioperative morbidity and mortality and this category of patients can benefit the most with the extraperitoneal approach. The purpose of this study was to evaluate the feasibility and the safety of extraperitoneal open RC in our experience; Methods: We retrospectively collected the data of 75 frail patients who underwent extraperitoneal open RC, performed by a single experienced surgeon. We assessed the frailty status using the simplified frailty index (sFI). We recorded data regarding general characteristics, intraoperative, pathological, postoperative complications and mortality (within 90 days); Results: We analyzed 61 men and 14 females with sFI equal or higher than 3. The median age was 77 years. Fifty-one patients had ASA score of 3 or more. Sixty procedures were with radical intention, while fifteen were palliative. Cutaneous ureterostomy was performed in 70 cases and extraperitonized ileal conduit in five cases. The median operative time was 150 minutes. The median blood loss was 400 mL. The median time up to flatus was 2 days. The median postoperative stay was 7 days. Thirteen patients had Clavien-Dindo 3 or 4 complications. Two patients died in first 90 days postoperatively; Conclusions: The extraperitoneal open RC in frail patients demonstrated to be a feasible and safe alternative approach in definitive or palliative setting in our experience.

Zinc Oxide nanoparticles functionalized with APTES were obtained to evaluate their CH4 and CO2 adsorption and separation behavior. First, ZnO nanoparticles were obtained by a precipitation method and subsequently coated with (3-aminopropyl)-triethoxysilane (APTES). The nanoparticles sample was characterized by UV-Vis, FT-IR spectroscopy, TGA, XRD, TEM/EDX, XPS and N2 adsorption at 77 K. CH4 and CO2 adsorption experiments were carried out at 298 K in a range between 0-10 bar. It was observed that the amount of gas adsorbed on the surface of the nanostructure is very small in comparison with other materials traditionally used for this purpose but slightly higher than those obtained in naked nanoparticles evaluated in previous studies.

Bloody stools in the newborn may be a clue for several clinical entities of varying severity ranging from a transient hematochezia to food protein induced allergic proctocolitis (FPIAP) or necrotizing enterocolitis (NEC). To distinguish among them at an early stage is challenging, but crucial, as the treatment and prognosis are different. We conducted a monocentric retrospective study including all preterm infants admitted to the NICU of the Vittore Buzzi Children’s Hospital (Milan) with bloody stools from December 2022 to May. Patients diagnosed with NEC exhibited a significantly lower eosinophil count and higher procalcitonin levels than both patients with CMPA and patients with transient hematochezia, as well as a statistically significant increase in pathological features at abdomen ultrasounds and abdominal X-rays. In contrast, no Lab markers or imaging have been demonstrated to be useful in distinguishing between transient hematochezia and FPIAIP. Thus, after excluding a diagnosis of NEC the only way to confirm the FPIAP is the oral food challenge, which can be performed even in premature newborns presenting blood stools, otherwise healthy, under medical supervision, in order to identify infants who may benefit from a cow’s milk free diet.

Hyperspectral imaging data holds great potential for various stages of the mining life cycle in active and abandoned mines. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite im-agery yields high spectral resolution, high signal-to-noise ratio (SNR), and global availability with breakthrough satellite sys-tems like EnMAP, EMIT, and PRISMA, limited spatial resolution poses challenges for mining sectors, which require decimeter- to centimeter-scale spatial resolution for applications such as reconciliation, ore/waste estimates, geotechnical assessments, and en-vironmental monitoring. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the pit/ mine scale, with the capability for frequent, user-defined re-visit times. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers to detect different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we review of applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging in or appropriate to the mining sector, especially for hyperspectral VNIR-SWIR UASs. If directly applicable, draw on previous insights derived from airborne, satellite, and ground-based imaging systems. We also discuss common practices for UAS survey planning and sampling considerations for interpretation.

This study utilized phytochemical screening to conduct qualitative analysis of plant extracts, aiming to identify various classes of secondary metabolites. Moreover, the antibacterial activity of different types of O. vulgare and S. triloba extracts was determined. To achieve the aim of the study, aqueous, ethanolic, and enzymatic extracts were prepared and screened for phytochemical capacity and antioxidant activities. Determination of the antibacterial activity included phenotypic screening of antibiotic susceptibility pattern of oral and food pathogenic bacterial strains, determination of Minimum inhibitory concentration and minimum bactericidal concentration - Microdilution broth test and in vitro valuation of antibacterial efficacies - anti-biofilm properties of the studied herbal extractions. Results: Our study evaluated the phytochemical composition and the antioxidant, antibacterial, and anti-biofilm properties of O. vulgare and S. triloba extracts. The analyzed samples contained bioactive compounds, such as phenolics and flavonoids, contributing to the observed strong antioxidant effect. Furthermore, they exhibited notable activity against oral biofilm formation and demonstrated significant antibacterial efficacy against dental caries microorganisms as well as food pathogens. Despite methodological variations, all extracts showed significant antioxidant capacity and promising antibacterial activity against various pathogens, including resistant strains, while also inhibiting biofilm formation. Although limited to two plant species and facing methodological constraints, the study lays a groundwork for future research, indicating the therapeutic potential of O. vulgare and S. triloba extracts. Further exploration is needed to report on underlying mechanisms and validate efficacy through clinical trials.

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

The beneficial effects of Lanthanide incorporation into 0.94Na0.5Bi0.5TiO3-0.06BaTiO3 (BNT-BT) matrix on its functional properties were investigated. The conventional solid-state method was used for synthesizing samples. The structural refinement revealed that all samples crystallized in R3c rhombohedral symmetry. Raman spectroscopy study was carried out using green laser excitation and revealed that no clear perceptible variation in frequency is observed. Dielectric measurements unveiled that the introduction of rare earth obstructed the depolarization temperature promoted in BNT-BT with decreasing the diffusive phase transition with increasing the lanthanide size. Only Dysprosium addition showed comparable diffusion constant and dielectric behavior as the unmodified composition. Further, the comparison of the obtained ferroelectric hysteresis and strain-electric field loops revealed that only Dy-phase exhibited interesting properties comparing parent composition. In addition, the incorporation of lanthanides Ln3+ into the BNT-BT matrix led to the development of luminescence characteristics in the visible and near infrared regions, depending on the excitation wavelengths. The simultaneous occurrence of photoluminescence and ferroelectric/piezoelectric properties opens up possibilities for BNT-BT-Ln to exhibit multifunctionality in a wide range of applications.Keywords: Sodium Bismuth Titanate; structural refinement; dielectric properties; ferroelectric properties; piezoelectric properties; Photoluminescence.

Child labor remains a predominant issue in Pakistan, despite the country’s existing policies and frameworks aimed at abolishing it. Through this study, we investigated the child labor distribution across Sindh and examined the factors that shape the regional patterns. We analyzed the data available through the 2018-19 Sindh Multiple Indicator Cluster Surveys, MICS 6, from 20,030 households with 40,633 children in the 5-17 age bracket. By applying prevalence statistics, chi-square tests, and regression modeling to these data, we investigated the trends in child labor prevalence, identified the correlation between child labor and various socioeconomic and geodemographic variables, and finally mapped the geospatial patterns of child labor across districts in Sindh, enabling us to identify and prioritize the districts in need of immediate intervention. The findings revealed that about 20 percent of the children in Sindh are engaged in child labor, with a high prevalence among males and in the 15-17 age bracket. Moreover, poverty and rural dwellings raise this issue. Other socioeconomic and geographic factors reinforcing this issue are lack of education among children, mothers or caretakers, and mothers’ or caretakers’ functional difficulties. However, children’s functional difficulties lower their prevalence in labor. Among the 29 districts across Sindh, Kambar Shahdadkot has the highest prevalence of child labor.

High-resolution (2 km) high-frequency (hourly) SST data from 2015-2021 provided by the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 geostationary satellite were used to study spatial and temporal variability of the China Coastal Front (CCF) in the South China Sea. The hourly SST data were processed with the Belkin and O’Reilly (2009) algorithm to generate long-term mean monthly maps of SST gradient magnitude (GM) and frontal frequency (FM). The horizontal structure of CCF was investigated from cross-frontal distributions of SST along 11 fixed lines that allowed to determine inshore and offshore boundaries of the CCF and calculate the CCF strength defined as the total cross-frontal step dSST = Offshore SST – Inshore SST. Combined with the results of Part1 of this study (Belkin, Lou, Yin, 2023), where the CCF was documented in the East China Sea, the new results reported in this paper allowed the CCF to be reliably traced from the East China Sea via Taiwan Strait into the northern South China Sea and farther west up to the east coast of Hainan Island.

Clostridial infections in cattle are a significant concern for Brazilian livestock. These diseases are caused by various species of Clostridium, which are known for their ability to produce potent toxins. Botulism in cattle is a serious and often fatal condition caused by the ingestion of neurotoxins produced by C. botulinum. This bacterium thrives in decomposing organic matter, such as spoiled feed, carcasses, and contaminated water. Tetanus while less common, tetanus is a serious disease following contamination of wounds with Clostridium tetani spores. It results in muscle stiffness, spasms, and often death due to respiratory failure. Blackleg (C. chauvoei) is this disease primarily affects young cattle, leading to acute lameness, swelling, and high fever. Malignant edema (C. septicum and others) is characterized by rapid onset of swelling at wound sites, malignant edema can occur after injuries or surgical procedures. Enterotoxemia is triggered by the rapid growth of C. perfringens in the gut following excessive carbohydrate intake. This leads to toxin production that causing sudden death. In conclusion, clostridial bovine infections remain a persistent challenge for Brazilian cattle farmers. With continued focus on vaccination, good management practices, and research, the impact of these diseases can be minimized, safeguarding the livestock industry's economic viability.

A thorough understanding of the type and severity of cataracts is crucial for accurately estimating the optimal phacoemulsification energy. In preceding research efforts, an innovative clinical prototype known as the Eye Scan Ultrasound System (ESUS) was developed to facilitate the automated characterization of cataracts. To evaluate the effectiveness of the prototype as a medical tool, extensive data must be collected from several patients with and without cataracts. However, obtaining an adequate number of patients and data for training and testing machine learning models is challenging. To overcome this limitation, the authors implemented a simulated prototype model of the ESUS system, to augment the data. The proposed model encompasses the electric-to-acoustic signal conversion in the ultrasonic transducer, the wave propagation through the eye, and the subsequent acoustic-to-electric signal conversion. Electrical modelling of the transducer was done using a two-port network and the wave propagation was modelled by using the k-Wave MATLAB toolbox. This holistic modelling approach enabled the generation of synthetic data augmentation, presenting great similarity with real data. The synthetic data can then be employed together with real data for the purpose of cataract classification.

The integration of supply chain data analytics with marketing strategies has become increasingly critical for organizations seeking to enhance customer insights and competitive advantage in today's dynamic business environment. This qualitative research explores the intersection of these two disciplines, focusing on implementation strategies, benefits, challenges, and future directions. Through semi-structured interviews with industry experts and stakeholders, key themes emerged, highlighting the importance of cross-functional collaboration, technological integration, and robust data governance in achieving effective integration. The study identifies significant benefits, including enhanced customer insights, improved operational efficiencies, and competitive advantage through personalized marketing strategies and optimized supply chain management. However, challenges such as data silos, technological complexities, and regulatory compliance issues pose barriers that require strategic solutions and organizational commitment. Future directions point towards the continued evolution of predictive analytics, AI-driven solutions, and sustainability integration, shaping the future landscape of supply chain management and marketing. Proactive engagement with regulatory frameworks and ethical considerations will be crucial in navigating these challenges and building trust with consumers. Ultimately, this research contributes to a deeper understanding of how organizations can leverage data analytics to drive strategic decision-making, enhance customer relationships, and achieve sustainable growth in an increasingly data-driven economy.

This paper provides a retrospective analysis of published COVID-19 data and focuses on Costa Rica as an example of data analysis and validation in developing countries. The COVID-19 outbreak has highlighted the importance of robust health systems and adequate resources to test and manage diseases. However, due to limited resources and excessive burdens, developing countries face unique challenges in this regard. This makes it difficult to conduct widespread testing. The lack of testing kits, laboratory facilities, trained personnel, and the necessary infrastructure is a significant barrier. Additionally, logistical issues such as transportation and distribution of testing kits in remote areas further complicate the situation. On the other hand, universal healthcare in developing countries, despite the lack of extensive testing, can still provide reliable data on the spread and impact of the disease. Universal healthcare can lead to better records keeping and data collection, as all individuals, regardless of their economic status, have access to medical services. This inclusivity ensures a more comprehensive and representative data set. Therefore, while testing is an important tool in managing the pandemic, universal healthcare systems can still provide valuable insights in its absence. This description can be applied to the Costa Rica COVID-19 scenario: lack of resources for an adequate testing methodology but a universal healthcare system that ensures inclusivity. In this work, we show that the number of active cases of COVID-19 for Costa Rica does not provide reliable information that can be used to adjust the parameters of a mathematical model, so the effectiveness of the contention measures cannot be precisely stated. However, the number of hospitalizations is more reliable in the centralized and universal healthcare system, but quantifying the effectiveness of such measures requires a different set of tools. Traditionally, most pandemic data analysis is based on the estimation of the Rt number based on the number of active cases. We propose a methodology based on digital signal processing principles and elementary differential calculus. To our knowledge, these kinds of methodologies have not been of widespread use in pandemic data analysis, and, as we show, they may provide a valuable descriptive analysis of pandemic tendencies and they are an accessible, fast and reliable decision-making tool.

In the context of the Korean building structure standard KDS2022, wind loads can be readily calculated in terms of the along-wind, across-wind, and torsional components, based on a single wind direction. However, this approach falls short in accurately and safely evaluating the impact of winds from various directions. Therefore, in this study, we developed a Wind Load Calculation Program (WCP) capable of predicting wind loads with relative precision during the preliminary design phase. First, wind tunnel tests were conducted to identify the essential factors necessary for calculating wind loads and the variables influencing these factors. The building shape considered was a square, and the wind force coefficients and power spectral density were measured by combining 4 ground roughness values, 11 side ratios (), 4 aspect ratios (), and wind directions ranging from 0° to 90°. Subsequently, a generalized formulation was devised to facilitate wind load calculations across diverse conditions and a method for calculating load combination coefficients, considering response correlation and load simultaneity, was presented. Finally, the wind loads obtained from the wind tunnel tests were compared with those predicted by the WCP using an actual project model.

The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRFTOA) has drawn a significant amout of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatial resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLRclear). Consequently, this study addresses the challenge of constructing LCRFTOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLRclear based on SBDART radiation transfer model (RTM), we developed a model for estimating OLRclear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLRclear results showed an RMSE of 5.05 W/m2 and MBE of 1.59 W/m2 compared to ERA5. Besides, the calculated LCRFTOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRFTOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRFTOA due to variaty cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation.

This paper presents a groundbreaking approach to real-time image processing in electronic component assembly, enhancing quality control in manufacturing. By capturing images from pick and place machines between component pickup and mounting, defects are identified and addressed in-line, significantly reducing the likelihood of defective products. Leveraging fast network protocols like gRPC and orchestration with Kubernetes, along with C++ programming and TensorFlow, this method achieves an average turnaround time of less than 5 milliseconds. Tested on 20 live production machines, it ensures compliance with IPC-A-610, and IPC-STD-J-001 standards while optimizing production efficiency and reliability.

Glaciers in northern Pakistan are a prime source of fresh water, providing headwater in the Indus River System and serving as a lifeline to millions of people in the region. These glaciers undergo continuous changes by melting due to global warming or accumulation due to snowfall/precipitation at higher altitudes. In this study, we used remote sensing data to quantify glacier changes in spatiotemporal variability in the past three decades. Five glaciers in the Gilgit region (near the junction of the Hindukush and Karakoram Mountains) with an extent of less than 5 square kilometers were selected, namely Phakor glacier, Karamber glacier, East Gammu glacier, Bhort glacier, and Bad-e-Swat glacier. The fluctuations in these glaciers were monitored using a digital elevation model (DEM) and a cloud-free continuous series of Landsat satellite pictures from the minimal snow cover season. The annual climatic trends were studied through spatially interpolated gridded climate data WοrldClim version-1 climate database for 1970 – 2000. We used it to study the variations of minimum and maximum temperature, solar radiation, and precipitation by preparing sub-sets from the original global grids. The temporal assessment of Bhort, Bad-e-Sawat, East Gammu, Karamber, and other rivers was also carried out. It is observed that the glaciers in the Gilgit watershed are rather stable. The little variability of glaciers is due to their geographic condition, altitude, topography, and orientation. The mapped glacier classes have been validated to check the accuracy assessment through an error matrix method. The kappa coefficient from the error matrix has been calculated to be 84 %. The study makes a critical input to a greater understanding of watershed controlling and hydrological processes in the upper Indus catchment's Gilgit watershed.

In the present work, a methodology for wind field reconstruction based on the Meteo Particle Model (MPM) from Numerical Weather Prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP Limited Area Models (LAMs) are run at spatial resolution of about 1 km, this level of information is not sufficient for many applications, for example to support drone operation in urban contexts. The coupling of MPM with the NWP Limited Area Model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values in the generical point considered for the investigation. The numerical results obtained reveal a good behavior of the method in reproducing the general trend of wind speed, as confirmed also by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of spatial and temporal resolution even if the MPM inherits the bias that inevitably affects the COSMO output.

Urbanization triggers the emergence of slums in urban areas. Palembang is one of the Indonesian cities with slum settlements. This research aimed to analyze the interpolation of slum positions in Palembang and their distribution pattern using kernel density analysis and by modeling spatial data on accuracy values. It employed a quantitative method with a survey approach. Samples were selected using proportional random sampling from families in 64 slum areas across the 13 districts in the city, and their positions were recorded with a GPS device and then processed and mapped in ArcGIS. The data were analyzed with inverse distance weighted, kernel density, and spatial data modeling. Results showed that slum areas near the riverbanks had high population density, while those located further were lower. The values obtained from the kernel density analysis varied from 0 to 58.1123, while the inverse distance weighted showed a value range of 2.26745–380.991. The spatial data modelling analysis demonstrated that the distribution of slum areas was widely spread along the Musi River, with an accuracy value of 96.8%, which falls within the range of >0.9-1.

CXCL14 is one of the most evolutionarily conserved members of the chemokine family and is constitutionally expressed in multiple organs, suggesting that it is involved in the homeostasis maintenance of the system. CXCL14 is highly expressed in colon epithelial cells and shows obvious gene silencing in clinical colon cancer samples, suggesting that its silencing is related to the immune escape of cancer cells. In this paper, we analyzed the expression profiles of multiple human clinical colon cancer datasets and mouse colon cancer models to reveal the variation trend of CXCL14 expression during colitis, colon polyps, primary colon cancer, and liver metastases. The relationship between CXCL14 gene silencing and promoter hypermethylation was revealed through the colorectal carcinoma methylation database. The results suggest that CXCL14 is a tumor suppressor gene in colorectal carcinoma, which is activated first and then silenced during the process of tumor occurrence and deterioration. Promoter hypermethylation is the main cause of CXCL14 silencing. The methylation level of CXCL14 is correlated with the anatomic site of tumor occurrence and positively correlated with patient age, and associated with prognosis. Reversing the hypermethylation of CXCL14 may be an epigenetic therapy for colon cancer.

Snow parameters have traditionally been retrieved using discontinuous, multi-band sensors; however, continuous hyperspectral sensor are now being developed as an alternative. In this paper we investigate the performance of various sensor configurations using machine learning neural networks trained on a simulated dataset. Our results show improvements in accuracy of retrievals of snow grain size and impurity concentration for continuous hyperspectral channel configurations. Retrieval accuracy of snow albedo was found to be similar for all channel configurations.

Background: Waste management is one of the challenges facing cities around the world. In Africa, where the increase in the urban population and the growing demand for consumer goods are driving up the rate of waste production, waste collection and disposal are posing serious problems, not only for municipal and central government officials, but also and above all for the local population. In Togo, and more specifically in the city of Lomé and the surrounding area (Greater Lomé), as in other African cities, the average annual production is estimated at 305340 tonnes, of which 89428 tonnes are collected and buried, giving a collection rate of 29.29%. A tiny proportion of the waste collected (9,000 tonnes, or 2.95%) is recycled as compost by associations and/or NGOs. The three key players who share this task are the local authority, the formal sector and the informal sector. Objective: The aim of this study is to assess the current state of waste management in Greater Lomé, which poses a major threat to public health, socio-economic development and the environment. Material and methods: To this end, documentary research and surveys carried out on two types of waste management stakeholder, namely formal and informal, in the five districts of Greater Lomé enabled us to make a general diagnosis. Results: However, despite the efforts made by the stakeholders involved in waste management, it appears that the transformation of solid household waste into compost remains the most suitable endogenous method for recovering fermentable waste and returning it to the soil, in order to improve the organization of waste management in the city of Lomé, reduce the quantity of waste to be landfilled and clean up the city. KEYWORDS: Urban waste, management, composting, collection, treatment, autonomous district of Greater Lomé.

Today electricity is distributed to the individual users via physical Copper connections. From the power stations, the energy is transported through transmission lines where the electricity, sinusoidally modulated at a fixed frequency of about 50 Hz, is raised up to a voltage in a range approximately variating between 30, and 110 kV. When electrons, aligned at this voltage, pass through the transmission lines, they cause coherent vibrations, sometimes even audible in the acoustic frequencies. In the spectrum of movements to which electric cables are subjected, there are also unexpected and sporadic movements caused by winds and rain. The problem of carrying out persistent, precise and spatially distributed monitoring of transmission line health is to use a large number of sensors physically distributed everywhere and somehow transmit the results to a control station. The use of synthetic aperture radar (SAR) data could be a viable solution of persistent and wide-area high-voltage vibration monitoring. In this paper we produce a comprehensive survey on how to monitor both movements in terms of vibrational displacement, due to weathering, and those due to electricity transmission, normally fixed at about 50 Hz. The results demonstrate the technical feasibility of an operational use of SAR for these purposes in the very short term. This would allow the possibility of monitoring malfunctions such as black-outs or other frequency anomalies.

Integrating voice assistant technology can potentially enhance accessibility and user interaction in web-based platforms for geospatial data visualisation and processing. This paper presents an open-source development incorporating a voice virtual assistant into the BStreams web application platform, designed specifically for visualising geospatial data. The study aimed to mitigate the observed limitations of virtual assistants in understanding the semantics and context of user commands in the geospatial domain. A discourse framework specialised for geospatial data was developed using a structured prototype methodology. In addition, a survey assisted in identifying commonly used English terminologies for geospatial data interaction. The questionnaire results were subsequently assessed using the ChatGPT natural language processing model. The application used open-source frameworks, including Web Speech API, Leaflet, Mapbox geocoding, and JavaScript. This investigation indicates that voice virtual assistants can potentially enhance human-computer interaction for geospatial data visualisation and processing. However, challenges related to natural language understanding, accents, and domain knowledge were raised. The paper advocates for further research to refine the integration of voice technology in this domain.

This research is a preliminary phase of a general effort to develop a generic-data transferring capability via human haptic thermal sensation (generic-data refers to a coded language like Morse or Braille). For the capability to be effective, it must include a large variety of short recognizable cues. Hence, we propose the concept of cues based on sequences of thermal pulses, i.e., combinations of warm and cool pulses with several levels of intensity. The objective of this study was to determine the feasibility of basing a generic-data-transfer capability on thermal cues composed of sequences of short pulses. The research included defining the basic characteristics of the stimuli parameters and developing practical methods for generating and measuring them. Several sequences were designed in light of the relevant data known to date, and tests were conducted. The thermal cues presented to the participants were sensed and recognized by touch. The results indicate high feasibility for a capability that is applicable in various scenarios. In addition, the low impact on human skin temperature due to short stimuli duration represents an inherent advantage for later implementation. This report presents promising findings and offers insights for further investigation.

Fish tissue samples from 203 adult individuals were collected in the main ports and markets of the Pacific coast of Panama. Molecular identification based on cytochrome oxidase I gene segment of all species was verified GENBANK reference sequences. 34 species from 14 families (Ariidae, Lutjanidae, Caranjidae, Scianidae, Centropomidae, Serranidae, Scombridae, Sphyraenidae, Haemulidae, Gerreidae, Stromotidae, Lobotidae, Malacanthidae, Mugilidae) were identified at species molecular level from 164 sequences. Also, three Caribbean species were molecularly identified among the analyzed samples (Mycteroperca xenarcha, Paralonchurus brasilensis and Lobotes surinamensis). Species diversity was slightly higher in the Gulf of Panama than in the Gulf of Chiriquí. For species with 5 or more individual sequences, genetic diversity and genetic connectivity parameters such as: total number of haplotypes (H), haplotype diversity (Hd), and nucleotide diversity (π) were calculated. Overall, pelagic-migratory species showed higher values of genetic diversity than coastal and estuarine species with some exceptions. Connectivity between Gulf areas was compared using values of genetic distances and genetic differentiation (Fst). High level of connectivity observed between Gulf of Chiriqui and Gulf of Montijo indicates the existence of a single stock in that area for the following species: Scomberomorus sierra, Caranx caninus and Lutjanus guttatus. Demographic history of the most common species was examined using Tajima’s D values suggesting population expansion for two snapper species, L. peru and L. argentiventris, having significant and higher values. Another important contribution from this research was the production of primers and dual labeled probes for environmental DNA detection using qPCR for the five most abundant species (spotted rose snapper, yellow snapper, green jack, Pacific crevalle jack and the Pacific sierra fish). These markers represent a new set of tools for environmental DNA (eDNA) detection and molecular traceability of three commercially important fish species along the supply chain including landing sites and markets of the main fishery areas.

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-kilometer resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011-2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

This study evaluated the effectiveness of an electronic system for managing drug-sensitive pulmonary tuberculosis patients in the Kyrgyz Republic. This was a cohort study using programmatic data. The study included patients registered on the paper-based system in 2019 and the electronic system between June 2021 and May 2022. Data was taken from 302 individuals from the electronic system for completeness and concordance in comparison with the paper-based system. The study showed that for most variables the completeness and concordance was 85.3%-93.0% and was lowest for non-mandatory fields such as medication side effects (26.8% vs 13.6%). No significant difference was observed in time taken from symptom onset to diagnosis and treatment initiation between the two systems. However, the electronic system had a significantly higher percentage of patients who initiated treatment on the same day of diagnosis (80.3% vs. 57.1%). The proportion with successful outcomes was similar in both groups, but the electronic system had a significantly lower proportion of patients with outcomes that were not evaluated or recorded (4.8% vs. 14.3%, p &lt; 0.001). The study highlights the potential advantages of implementing an electronic TB register system for improving records. We advocate for similar studies in other regions in Kyrgyz Republic.

Abstract: Our research analyzed the characteristics of phase IV clinical trials in oncology using data from the ClinicalTrials.gov registry. The included trials were conducted between January 2013 and December 2022 and were examined for key characteristics, including outcome measures, interventions, sample sizes, and study design, different cancer types, and geographic regions. Our analysis included 368 phase IV oncology studies. We found that 50% of these studies aimed to examine both safety and efficacy, while 43.5% only reported efficacy outcome measures, and 6.5% only described safety outcome measures. Only 16.9% of studies were powered to detect adverse events with a frequency of 1 in 100. Targeted therapies accounted for the majority of included studies (53.5%), with breast (32.91%) and hematological cancers (25.82%) being the most frequently investigated malignancies. Most phase IV oncology studies lacked sufficient power to detect rare adverse events due to their small sample sizes and instead focused on effectiveness. To ensure that there is no gap in drug safety data collection and detection of rare adverse events due to limited phase IV clinical trials, there is a significant need for additional education and participations by both health care providers and patients in spontaneous reporting processes.

Understanding epigenome paths through which smoking contributes to cardiometabolic traits is important for downstream applications. In this study, I used a SNP-based analytical pipeline to integrate several publicly available datasets and identify CpG sites that mediate the impact of smoking on cardiometabolic traits. After applying stringent statistical criteria, 11 CpG sites were detected that showed significant association (P<5e-8) with cardiometabolic traits at both discovery and replication stage. By integrating eQTL data, I identified genes behind a number of these associations. cg05228408 was hypomethylated in smokers and contributed to higher blood pressure by lowering the expression of CLCN6 gene. cg08639339 was hypermethylated in smokers and lowered metabolic rate by increasing the expression of RAB29; furthermore, I noted TMEM120A mediated the impact of smoking-cg17325771 on LDL, and LTBP3 mediated the smoking-cg07029024 effect on heart rate. This study provides a list of CpG sites that mediates the impact of smoking on cardiometabolic traits and a framework to investigate epigenome path through which a lifestyle habit modifies disease risks.

Background: The prevalence of physical inactivity has been rising in many countries in recent years, adding to the burden of non-communicable diseases and affecting overall health worldwide. The aim of this study is to determine the impact of physical activity on mortality from ischemic heart disease (IHD) separately for those respondents who were diagnosed with IHD and for those who were not diagnosed with IHD in their baseline health survey. Methods: In the baseline survey (2006–2008), 7100 men and women ages 45–72 were examined within the framework of the international study Health, Alcohol, and Psychosocial Factors in Eastern Europe (HAPIEE). 6770 participants were available for statistical analysis (after excluding 330 respondents due to missing information on study variables). Physical activity was determined by leisure-time physical activities (hours/week). All participants in the baseline survey were followed up for IHD mortality events until December 31, 2018. Results: Using multivariate Cox regression analysis, it was found that moderate and higher levels of physical activity significantly reduced the risk of IHD mortality (HR=0.54 and HR=0.60 respectively) in men who were not diagnosed with IHD at baseline compared with physically inactive subjects. It was found that among men and women who were diagnosed with IHD at baseline, physical activity reduced the risk of mortality from IHD compared with those who were physically inactive (HR=0.54 and HR=0.41 respectively). Using mediation analysis, was found that physical activity directly predicted statistically lower IHD mortality (P&lt;0.05) in men and women. Conclusion: Physical activity was a significant factor that directly predicted statistically lower IHD mortality, regardless of whether subjects had IHD at baseline or not.

This article presents a review of the distribution system state estimation (DSSE) algorithms from the model-based approach to the recent data-driven methodologies. The insufficient measurements which result in low observability have motivated the need to shift from the conventional algorithm to data-driven approaches that can successfully estimate states despite the DSSE challenges. This article discusses the nonlinearity in the DSSE problem formulation and how different model-based methods have been proposed to mitigate the problems of robustness, ill-conditioning, and complexity. Moreover, approximate DSSE to obviate nonlinearity were discussed: complex linearization, small angle approximation, convexification and compressed sensing. Furthermore, probabilistic DSSE methods were also discussed in the need to quantify the uncertainty associated with the state estimation results. Also, data-driven methods applicable to DSSE, pseudo measurement generations, and topology identification were also discussed using machine and deep learning methods. Lastly, a recent approach that employs a hybrid of model-based and data-driven methods using matrix and tensor completion is surfacing because they work in a low observable condition of the network and can estimate the states satisfactorily. With this review, researchers can look further into developing and improving on the model-based data-driven methods less susceptible to the barriers in conventional DSSE algorithms.

The study is based on the notion that big data predictive analytics is important for developing strategic alliances performance of companies. this study investigates the relationship between big data predictive analytics, big data culture, and competitive strategies 'techniques were adopted, such as descriptive statistics, correlation,regression, etc. using SPSS and SmartPLS statistical software. Hypotheses were tested with bootstrapped analysis using SEM (through SmartPLS). The study developed a structural equation model by using the SEM analysis. The results of the SEM analysis suggested the hypothesized model of the study was valid. The results supported all the hypotheses of the study.Through empirical analysis, demonstrated the conclusion is that the big data predictive analytics has a positive and significant relationship with strategic alliance performance

MSMEs play an important role in the global economy, employing nearly 90% of the workforce and contributing nearly 50% of the world's gross domestic product. However, many MSMEs have difficulty finding and retaining repeat customers, which is one of the main challenges for small businesses. This study aims to find out how customer data can be used to improve marketing strategies and meet customer needs and preferences more effectively. This research also aims to provide solutions and recommendations for MSMEs to improve their marketing through personalizing the experience for their customers. This study uses the SEM-PLS method with a quantitative approach. The population is taken from SMEs operating in the city of Semarang. 100 Samples are determined through Quota Sampling. Personalized marketing involves using customer data to customize the customer experience, which can create a closer relationship between customers and MSMEs. This research is still limited to the effectiveness of Personalized Marketing on customer loyalty in MSMEs. There have been many studies that discuss MSME customer loyalty with the marketing communication method. However, there are not many studies that specifically discuss personalized marketing for MSMEs.

The information data accuracy affects the working reliability of production management system directly. Mobile equipment is an important part of integrated manufacturing system, in the course of its works, due to the complex working environment and the dynamic change of its position, it lead to the important problem of information detection distortion. Because the application of fractional differential operator has the dual functions of enhancing signal strength and reducing data error, we puts forward a data processing method of obtaining production information data of various accuracy by changing the parameters of fractional differential operator. Establish a mobile equipment detection data fusion processing model based on fractional order partial differential equations and apply it in the processing experiment of mobile equipment testing information detection data in an integrated manufacturing system, the feasibility and effectiveness of the method are verified. We conclude that the fractional order partial differential equations used in the processing of mobile equipment monitoring information data of an integrated manufacturing system has the function of obtaining production information data of various accuracy, which are of great significance to improve the working reliability in integrated manufacturing system. which is of great significance to improve the working reliability and scientific decision of integrated manufacturing system.

Bayesian Maximum Entropy (BME) is increasingly used in predicting and mapping spatio-temporal data. However, studies that have fully evaluated its robustness empirically are rare. Therefore, this research examined empirically the effect of skewness, sample size and spatial dependency level using simulated data. We considered symmetric data, data positively skewed by 1, 3, 6 and 9, data with weak, moderate, and strong spatial dependency levels, and sample sizes from 100 to 500 at the interval length of 50. The results showed that the variation of sample sizes and spatial dependency levels do not affect the Mean Square Error (MSE) and bias of BME prediction. However, skewness affects the MSE of prediction but does not affect the bias. This result indicates that BME is robust to sample size and is unbiased. Despite the significant difference due to skewness, a graphical plot showed values of MSE close to zero, suggesting that BME can be considered robust to skewness.

Additive manufacturing (AM) are used in several industries such as automotive, aerospace, and medical sciences. One of the most common devices used in additive manufacturing is fused deposition modeling (FDM) 3D printers. This fabrication method has different inputs that affect the quality of the parts. In this research, the bending fatigue properties of polylactic acid (PLA) biomaterial made with a 3D printer are investigated. To demonstrate the influence of printing parameters on fatigue lifetime, standard specimens with nozzle diameters of 0.2-0.6 mm, extruder temperature of 180-240°C, and print speed of 5-15 mm/s were printed. After performing fully-reversed bending fatigue tests, it was found that printed specimens at 180°C have the best fatigue lifetime in most cases. Accordingly, fatigue behavior improved by reducing the nozzle diameter. Printing at lower temperatures also improved fatigue lifetime. The printing speed affected the slope of the S-N diagram, known as the fatigue strength exponent.

Room temperature superconductivity has recently been reported for a carbonaceous sulfur hydride (CSH) under high pressure by Snider et al [1]. The paper reports sharp drops in magnetic susceptibility as a function of temperature for five different pressures, that are interpreted as signaling a superconducting transition. Here I question the validity and faithfulness of the magnetic susceptibility data presented in the paper by comparison with the measured raw data reported by two of the authors of ref. [2]. This invalidates the assertion of the paper [1] that the susceptibility measurements support the case for superconductivity in this compound.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

Pulsed thermography is a commonly used non-destructive testing method, and is increasingly studied for advanced materials such as carbon fiber-reinforced polymer (CFRP) evaluation. Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, we used a type of matrix decomposition using Robust-PCA via Inexact-ALM in our experiment. We investigate this method as a pre-and post-processing method on thermal data acquired by pulsed thermography. We employed state-of-the-art methods, i.e., PCT, PPT, and PLST, as the main process. The results indicate that pre-processing on thermal data can elevate the defect detectability while post-processing, in some cases, can deteriorate the results.

Abstract. The purpose of this review is to investigate the nature of privacy concerns in the context of Smart Local Energy Systems (SLES) to understand how SLES providers can minimize both users’ concern, and cause for concern, around privacy. We conducted a rapid realist review and thematic framework analysis against Bronfrenbrenner’s socio-ecological model to understand privacy concerns in different contexts. A common privacy concern was that sharing detailed energy use data had the potential to reveal information about home life, and to intrude upon people’s sense of autonomy, choice and control. Evidence suggests that people are willing to accept newdata sharing technologies if the benefits of doing so are clear, anticipated, and mutually beneficial. Building trust, through increasing knowledge and understanding was a mechanism for overcoming privacy concerns, but this was mediated by the organization providing the information. Non-profit organizations were more trusted to ensure appropriate safeguards to privacy were in place. One key barrier to participation with good supporting evidence was that people can resist perceived intrusions on their privacy. This could be actively resisted by refusing to install data collection technologies or passively by non-participation in adapting energy use behaviors: both of which are necessary for SLES to achieve their goals of managing energy demand and building resilience in smart grids.

As a result of the influence of geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, the spatial structure of food culture represented by the cuisine culture at the regional level has not yet been explored from the perspective of geography. This study aims to explore such patterns by focusing on the restaurants of the eight most famous cuisines in Mainland China. Initially, the density based geospatial hotspot detector method is proposed to analyze and mapping the spatial quantitative characteristics of the eight major cuisines. A heuristic method for geographical regionalization based on machine learning was used to analyze spatial distribution patterns in accordance with the proportion of these cuisines in each prefecture-level city. Results show that some types of single-category cuisines have a stronger spatial concentration effect in the present, whereas others have a strong diffusion trend. In the comprehensive analysis of multicategory cuisines, the eight major cuisines formed a new structure of geographical regionalization of Chinese cuisine culture. This study is helpful to understand regional structure characteristics of food preference, and the density based hotspot detector proposed in this paper can also be used in the analysis of other type of POI data.

In this work, we treat a prediction problem via the conditional hazard function of a scalar response variable Y given a functional random variable X by using the local linear technique. The main purpose of this paper is to investigate the asymptotic normality of the nonparametric estimator of the conditional hazard function, under some general conditions. A simulation study, conducted to assess finite sample behavior, demonstrates the superiority of our method than the standard kernel method

The main goal of our work is the revealing of problematic issues related to estimates of the absorption coefficient of colored organic matter in the northern seas from data of the Ocean and Land Color Instrument (OLCI) on the Sentinel-3 satellites. In particular, a comparison of the OLCI standard error estimates ADG443_NN_err., relating to the measurement and retrieval of the geophysical products, with the uncertainties in the real situation of the northern seas, where the natural conditions are extremely unfavorable (first of all, frequent cloudiness, low Sun heights). We conducted a comprehensive multi-sensor study of the uncertainties using various approaches, first at all, directly comparing the data from satellite (OLCI Sentinel-3 and four other ocean color sensors) and field measurements in five sea expeditions 2016-2019, by using the different processing algorithms. Our analysis has shown that the real uncertainties of the final product are significantly higher than the calculated errors of the ADG443_NN_err., which is 100% and ~10%. The main reason for that is the unsatisfactory atmospheric correction. We present the results of the analysis of the different effecting factors (satellite sensors, processing algorithms, use of the other parameters), and formulate the tasks of future work.

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

Sentinel-1 satellite system continuously observes European countries in a relatively high revisit frequency of 6 days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for processing is an original data provided by ESA, for interferometry (InSAR) this level is a Single Look Complex (SLC) data. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nation-wide Sentinel-1 data stored in burst units that have been preprocessed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g. Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nation-wide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provide a signal for early warning against a potential dangerous displacement, e.g. a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nation-wide PS processing using data over Czechia has been performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

Olympic Games have a huge impact on the cities where they are held, both during the actual celebration of the event and before and after it. This study presents a new approach based on spatial analysis, GIS, and data coming from Location Based Social Networks to model the spatiotemporal dimension of impacts associated with the Rio 2016 Olympic Games. Geolocalized data from Twitter are used to analyze the activity pattern of users from two different viewpoints. The first monitors the activity of Twitter users during the event -the arrival of visitors, where they came from, and the use resident and tourist made of different areas of the city. The second assesses the spatiotemporal use of the city by Twitter users before the event, compared to the use during and after the event. The results not only reveal which spaces were the most used while the Games were being held but also changes in the urban dynamics after the Games. Both approaches can be used to assess the impacts of mega-events and to improve the management and allocation of urban resources such as transport and public services infrastructure.

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-seq is one of the most commonly used techniques to perform transcriptional experiments. Computational analysis on such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-seq data-sets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsig and identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50\% of the genes identified as influential have some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes have crucial roles or participate in processes associated with response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.

The world is facing new challenges every day; however, with the spread of the pandemic around the world, this new challenge is different. The pandemic is increasing and concentrating various challenges simultaneously. Although different sectors are facing consequences, the most important sectors, that is, health and economy are the most affected. When the pandemic began, it was not known how long it would last, which complicated health and economic planning. Therefore, it is important for decision makers and the public to know the predictions and expectations of the future of these challenges. In this work, the current situation is analyzed. Then, an expectation model is developed based on the statistics of the pandemic using a growth rate model based on an exponential and logarithmic rate of increase. Based on the available open data about the pandemic spread, the model can successfully predict future expectations, including the duration and maximum number of cases of the pandemic. The model uses the equilibrium point as the day the cases decrease. The model can be used for planning and the development of strategies to overcome these challenges.

Nutrient estimation in forest ecosystems through satellite images allows us to obtain accurate data, starting with data transformation from forest stands and the existing relationship with the spectral information of the image through modeling. The objective of the study was to quantify and validate the content of C, N, H in aboveground tree biomass in managed stands using spatial modeling and satellite images. This study was conducted during 2017-2018 in managed forest stands in San Juan Lachao, Oaxaca, Mexico. Fifteen 400 m2 experimental sites were selectively established, using a completely randomized experimental design of five silvicultural treatments with three replications. As part of data preprocessing, normality and homogeneity of variances assumptions were checked using the Shapiro-Wilk and Bartlett tests, respectively. From the pixels, data of the average of Normalized Difference Vegetation Index (NDVI) that surrounded the sampling sites were contrasted against the data obtained from forest inventory and the regression models to estimate C, N, H and biomass were generated. Models were validated by NDVI. With the models we estimated 0.95 t ha-1 biomass, which contains between 0.61 and 0.63 of C, 0.44-0.46 of N and 0.24 of H. The models generated had coefficients of determination (R2) of 0.85 to 0.87, which are significant parameters (p ≤ 0.0001). These results confirm that the use of Sentinel satellite images in the estimation of these elements in forest ecosystems based on the relationship between data inventory and the NDVI is highly reliable.

Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

The parameters of logistic regression models are usually obtained by the method of maximum likelihood (ML). However, in analyses of small data sets or data sets with unbalanced outcomes or exposures, ML parameter estimates may not exist. This situation has been termed “separation” as the two outcome groups are separated by the values of a covariate or a linear combination of covariates. To overcome the problem of non-existing ML parameter estimates, applying Firth’s correction (FC) was proposed. In practice, however, a principal investigator might be advised to “bring more data” in order to solve a separation issue. We illustrate the problem by means of an examples from colorectal cancer screening and ornithology. It is unclear if such an increasing sample size (ISS) strategy that keeps sampling new observations until separation is removed improves estimation compared to applying FC to the original data set. We performed an extensive simulation study where the main focus was to estimate the cost-adjusted relative efficiency of ML combined with ISS compared to FC. FC yielded reasonably small root mean squared errors and proved to be the more efficient estimator. Given our findings, we propose not to adapt the sample size when separation is encountered but to use FC as the default method of analysis whenever the number of observations or outcome events is critically low.

Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzo-p-furans (PCDD/Fs) are environmental pollutants with a great persistence, capacity of bioaccumulation, and well known important toxic effects in humans and animals. Incinerators of hazardous, municipal and medical waste, chlorine bleaching of paper pulp, cement plants, and the traffic of motor vehicles are the most frequent emission sources of these compounds. The diet, followed at a great distance by inhalation, is generally the main way of human exposure to PCDD/Fs. Human biomonitoring is of a great importance to prevent potential adverse effects derived from exposure to chemicals such as PCDD/Fs. In relation to this, blood is among the most used biological monitors. In the current review, we have summarized the recent information (2000-2009) published in the scientific literature (databases: Scopus and PubMed) on the concentrations of PCDD/Fs in blood samples of non-occupationally exposed populations, as well as in some groups of occupationally exposed individuals. We have revised a number of studies conducted in various African American, Asian and European countries, and Australia. Unfortunately, the information is quite limited. No data are available for most countries over the world. Based on the results here reviewed –where available- the current health risks for the general populations do not seem to be of concern. Moreover, taking into account the important reductions observed in the levels of PCDD/Fs in foodstuffs, new decreases in the concentrations of PCDD/Fs in blood -and other biological tissues- are very probable in the immediate years.

Remote sensing technologies may play a fundamental role in the environmental assessment of open-cast mining and the accurate quantification of mine land rehabilitation efforts. Here, we developed a systematic geographic object-based image analysis (GEOBIA) approach to map the amount of revegetated area and to quantify the land-use changes in open-cast mines in the Carajás region situated in the eastern Amazon. Based on high-resolution satellite images from 2011 to 2015 from different sensors (GeoEye, WorldView-3 and Ikonos), we quantified forests, cangas (natural metalliferous savanna ecosystems), mine land, revegetated areas and water bodies. Based on the GEOBIA approach, threshold values were established to discriminate land cover classes using spectral bands, and the NDVI and NDWI indices and LiDAR digital ground and slope models. The overall accuracy was higher than 90%, and the Kappa indices varied between 0.82 and 0.88. During the observation period, the mining complex expanded; for that, canga and forest vegetation was converted to mine land. At the same time, the amount of revegetated area increased. Thus, we conclude that our approach is capable of providing consistent information regarding land cover changes in mines, with a special focus on the amount of revegetation necessary to fulfill environmental liabilities.

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~ 182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to tree boundary while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

As a part of the proposed article, the authors presented comprehensive data analysis for movement data that comes from a positioning system based on ultra-wide band (UWB) technology. For purpose of this article, a test was carried out during which the car equipped with cruise control overcame the given path at a speed from 10 km/h to 60 km/h. The obtained motion models (information about position) have been filtered through a series of filters - from fundamentals filters with a variable window (median, moving average, Savitzky-Golay filter), through more complex ones like the Wiener or Kalman filter. As a result, the authors proposed a form of data analysis and filtration depending on the speed of the moving object. In addition, the maximum accuracy that can be obtained for a given traffic model was also determined. The whole research proves that it is possible to use a system based on UWB technology in positioning objects for urban applications - smart city, in industry 4.0 applications as well as for positioning autonomous vehicles in urban applications, such as well as on highways to maintain cohesion of convoys vehicles.

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environments under rapid urban expansion. Current MODIS data is, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data can’t explore temporally the refined analysis due to the low temporal resolution. In order to resolve this situation, we used MODIS and Landsat data to generate “Landsat-like” data by using the flexible spatiotemporal data fusion method (FSDAF), and then studied spatiotemporal variation of land surface temperature (LST) and its driving factors. The results showed that 1) The estimated “Landsat-like” data have high precision; 2) By comparing 2013 and 2016 datasets, LST increases ranging from 1.8°C to 4°C were measurable in areas where the impervious surface area (ISA) increased, while LST decreases ranging from -3.52°C to -0.70°C were detected in areas where ISA decreased; 3) LST has a strongly negative relationship with the Normalized Difference Vegetation Index (NDVI), and a strongly positive relationship with Normalized Difference Built Index (NDBI) in summer; and 4) LST is well correlated with Building density (BD), in a complex conic mode, and LST may increase by 0.460°C to 0.786°C when BD increases by 0.1. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scale up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom employing stakeholder interviews and user surveys, aiming at gaining insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities.

Quality control (QC) may be a lengthy and tedious process. As a result, most data users use data from meteorological services without performing data quality checks. The South African Weather Service (SAWS) re-established the national solar radiometric network comprising of 13 new stations within the six climatic zones of the country. This study reports on the performance results of the Baseline Surface Radiation Network (BSRN) QC procedures applied to the solar radiation data within the SAWS radiometric network. The overall percentage performance of the SAWS solar radiation network based on BSRN QC methodology is 97.79%, 93.64%, 91.6% and 92.23% for Long Wave Downward Irradiance (LWD), Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) respectively with operational problems largely dominating the percentage of bad data. The overall average performance of the Surface Solar Radiation Dataset – Heliosat (SARAH) data records for the GHI estimation for all the stations showed a Mean Bias Deviation (MBD) of -8.28 Wm^-2, a Mean Absolute Deviation (MAD) of 9.06 Wm^-2 and the Root Mean Square Deviation (RMSD) of 11.02 Wm^-2. The correlation (quantified by R²) between ground-based and SARAH-derived GHI time series was ~ 0.98. The established network has the potential of providing high quality minute solar radiation data sets (GHI, DHI, DNI and LWD) and auxiliary hourly meteorological parameters vital for scientific and practical applications in renewable energy technologies in South Africa.

Salinity intrusion is one of the most serious consequences of climate change coupled with rising sea level that significantly affects agricultural activities in many parts of the world. This phenomenon has increasingly become more serious and frequently occurred in the Mekong Delta of Vietnam. As a result, Vietnam has been ranked among top five countries where have been devastatingly impacted by climate change, in particular, its Tra Vinh Province characterized by coastal plain and alluvial deposit. In addition, this area is of the tropical monsoon zone of long rainy season with source of salt brought from the sea by the tides and sea level rise. Regions that are contaminated by salt are located in lowland and often suffer from floods linking to tidal effects with salty water from river systems and channels. Soil salinity evaluation is critical for coastal protection, restoration, and agricultural planning since it can be considered as an agricultural indicator to evaluate quality of soil. Here, we attempt to estimate the soil salinity in Tra Vinh Province, in the Mekong Delta of Vietnam. Landsat 8 OLI images are utilized to derive indices for soil salinity evaluation including single bands, Vegetation Soil Salinity Index (VSSI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Salinity Index (NDSI). Subsequently, satistical analysis between soil salinity, electrical conductivity (EC, dS/m), and environmental indices derived from Landsat 8 OLI image is performed. Results indicate that spectral value of Near Infrared (NIR) band and VSSI are highly correlated with EC (R2 = 0.7779 and R2 = 0.6957, respectively) in comparison with the other indices. Comparative results show that soil salinity derived from Landsat 8 is consistent with in situ data. Findings of this study demonstrate that Landsat 8 OLI images reveal a high potential for spatiotemporally monitoring the magnitude of soil salinity at the top soil layer. Outcomes of this study are useful for agricultural activities, planners, and farmers by providing the base map of soil salinity contamination for better selection of accomodating crop types to reduce economical lost in the context of climate change. Our proposed method that estimates soil salinity using satellite-derived variables can be applied in the other regions.

This study attempts to assess the impact of corruption on economic growth in the Mediterranean countries, during the period from 1998 to 2007. Econometric analysis using panel regression has been adopted to test this effect. Individual effects models such as random effects model and fixed effects model were applied to the study sample of 160 observations, and to choose the suitable model, we implemented several tests. For our analysis, we used a basic model that includes the dependent variable GDP per capita as a factor of economic growth and the corruption perception index as the independent variable concerned. Then we completed the model with several standardized macroeconomic control variables mentioned above and applied the individual effects models. The outcomes illustrate that corruption has a negative impact on the selected Mediterranean countries’ economic growth.

As a procedure deriving UH (unit hydrograph), the root selection method necessitates only storm runoff data. However, this method must deal with the uncertainty related to the noise fluctuation of runoff ordinates and derive one optimal UH from many storms. This study proposes a procedure that applies the Savitzky-Golay filter to smooth the noise fluctuation of the runoff ordinates and uses the linear combination of UHs from individual storms to derive an optimal UH. The proposed method is applied to the storms of the Nenagh River basin in Ireland. The applicability of the Savitzky-Golay filter for smoothing the noise fluctuation of storm runoffs is examined by means of the Nash-Sutcliffe efficiency index. Furthermore, the root selection method is extended to also estimate IUHs. The results show that the adoption of the Savitzky-Golay filter improves the applicability of the root selection method and that the optimal UH predicts accurately the time-to-peak and peak discharge.

The mortality of birds resulting from collisions and electrocutions with overhead lines, such as power lines, and phone line among others, has been implicated in the decline of various avian species globally. Specifically, overhead line collisions pose a significant threat to the conserva-tion of the Canarian houbara bustard (Chlamydotis undulata fuertaventurae), an endangered sub-species endemic to the Canary Islands. This study centres on the postmortem findings of Canarian houbara bustards that have collided with overhead lines, providing insights into the post-collision outcomes for these birds. A com-plete standardized necropsy of nine Canarian houbara bustards revealed that polytrauma was the cause of death in all cases. The most notable gross lesions associated with trauma included bone fractures, soft tissue lacerations, haemorrhages, luxations, and hemocoelom, with certain body regions being more frequently affected. Histopathology, immunohistochemistry, and en-tomology analysis confirmed that numerous birds survived the initial trauma, exhibiting varia-ble survival intervals before succumbing to their injuries. We concluded that when a houbara bustard collide with an overhead line it frequently survives the initial trauma. The histopathology, immunohistochemistry, or entomologic analysis may be helpful to approximate the timing interval between trauma and death.

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. The high material costs of the gas diffusion layer(GDL) and bipolar plate(BP) creates a barrier for large scale commercial application. This study aims towards addressing this challenge by optimizing the material and design of the cathode, GDL and BP. While Deterministic design optimization(DDO) methods have been extensively studied, they often fall short when manufacturing uncertainties are introduced. This issue is addressed by introducing Reliability based design optimization(RBDO) to optimize four key PEMFC design variables i.e., δ_gdl, d_ch, w_ch, and w_land. The objective is to maximize cell voltage(V_cell) considering material cost of cathode gas diffusion layer(Cost_cGDL), and cathode bipolar plate(Cost_cBP) as reliability constraints. Results of the DDO show an increment in V_cell of 31mV, with reliability of around 50% for Cost_cGDL and Cost_cBP. In contrast RBDO method provides a reliability of 95% for both Cost_cGDL and Cost_cBP.

This work presents emission line flux and kinematic structure mapping based on IFU data of the planetary nebulae Abell 30 (A30). The IFU has a field of view (FoV) of 16" x 12.3" and was used to partially cover knots J1 and J2, and completely cover knots J3 and J4 of A30. Our emission line flux map shows recombination species C II, C III, He I, O II as well as forbidden line species [O III], [Ne III] and [Ne IV] of high excitation level are all distributed in different parts of the knots. The kinematic structure shows considerable variation in both the velocities and spatial locations of these knots with positive and negative velocity excursions from the systematic value. Forbidden line species [O III], [Ne III] are distributed in both knots J3 and J4 but they are travelling at different speeds in opposite directions. Despite variation in distribution of the species, the velocities of [Ne IV] and [O III] are similar and travelling in the same direction while the other species [Ne III], He I, and O II share similar velocity of 200kms−1 up in the opposite direction. The complexity of distribution and kinematic structure of the knots revealed in this work indicates a strong tendency towards the hypothesis of multiple triggering events in the past history of A30.

Essential oils (EOs) have been gaining popularity in the past decades among researchers due to their potential to replace conventional chemicals used in the fight against pests, pathogenic and spoilage microbes and oxidation processes. EOs are complex mixtures with many chemical components the content of which depends on many factors – not just the plant genus, species or subspecies, but also chemotype, locality and climatic conditions, phase of vegetation, method of extraction and others. Due to this fact, there’s still much to study, with antimicrobial effect being one of the key properties of EOs. There are many methods that have been frequently engaged by researchers for in vitro evaluation; however, although the research has been going on for decades, an internationally accepted standard is still missing. Most of methods are based on time-proven standards used for testing of antibiotics. Due to the specific properties of EOs and their components, such as volatility and hydrophobicity, many modifications of these standard procedures have been adopted. The aim of this review is to describe the most common methods and their modifications for testing of antimicrobial properties of EOs and to point out the most controversial variables which can potentially affect results of the assays.

An agrotechnological district (ATD) integrates sustainable agricultural practices and technologies, aiming to transform rural communities by stimulating socioeconomic development and addressing the UN Sustainable Development Goals. Ingaí, a dairy-producing municipality in Minas Gerais, Brazil, stands to benefit from the implementation of an ATD. This study aimed to identify the opportunities and challenges for the implementation of an ATD in the municipality, considering its socioeconomic characteristics, level of producers' technological adoption, connectivity infrastructure, support networks, rural and market management. Through an exploratory case study, using semi-structured group interviews with agricultural stakeholders, key challenges emerged: limited connectivity infrastructure; ineffective property management; rural family succession problems; barriers in accessing credit; resistance to the adoption of technologies; and dependence on the local government. Opportunities included strengthening of support networks among the agents of rural productive activity; promoting technology adoption by producers; and creating training and rural extension programs. The adoption of these initiatives can stimulate the Ingaí´s economic and social development, generate multiplier effects in its region of influence, and serve as a model for other initiatives in the state of Minas Gerais, the main dairy hub in the country, and one of the primary drivers of Brazilian agribusiness.

Commercial Brazilian wheat flour was subjected to extrusion, oven, and microwave treatments. The solubility, monomeric and polymeric proteins, and glutenin and gliadin profiles of gluten were analyzed. In addition, in vitro digestibility and response against potential celiac disease immune-stimulatory epitopes were investigated. All treatments resulted in low solubility of the polymeric and monomeric proteins. The amounts of insoluble proteins increased from 5.6 % in control flour to approximately 10 % for all treatments, whereas soluble proteins decreased from 6.5 % to less than 0.5 % post treatment. In addition, treatments affected glutenin and gliadin pro-files. The amount of α/β- gliadin extracted decreased after all treatments, while that of γ-gliadin was unaffected. Finally, alteration of the gluten structure and complexity was not sufficient to render a product safe for consumption for individuals with celiac disease; the number of poten-tial celiac disease immune-stimulatory epitopes remained high.

Currently, the use of recycled waste plastic in road construction is not just considered ecofriendly but also considered advantageous for improving road performance over time. This research explores using recycled plastic bags, made of low-density polyethylene (LDPE), in asphalt mixtures to improve road performance and manage waste. The study aims to assess the effects of incorporating different amounts of shredded waste plastic into asphalt mixes and determine the optimal percentage. The aggregate used was obtained from Sargodha quarry Pakistan and asphalt used was 60/70 grade obtained from national refinery limited Pakistan. Using the Marshall mix design, the optimum bitumen content (OBC) was found to be 4.5% of the asphalt mix weight. Thirty samples were tested with waste plastic proportions of 6%, 8%, 10%, 12%, and 14% of OBC weight, along with a standard mix. Tests on stability, bulk density, flow, and air voids showed that a 10% plastic content significantly enhances asphalt performance, increasing stability by around 25%. The study recommends using 10% plastic by OBC weight in asphalt mixes for better performance and suggests further research on optimal incorporation techniques of waste materials in asphalt mixtures.

It is imperative to conduct experimental studies on the seepage behavior of gas-bearing coal under cyclic dynamic loading conditions. This paper focuses on the evolution of coal permeability under the combined effects of dynamic loading, static loading, and gas adsorption. The principal conclusions are as follows. (1) As the frequency and amplitude of dynamic loading increase, the development of pore and fissure structures within the coal body becomes increasingly pronounced during dynamic loading cycles, resulting in a gradual rise in permeability. Notably, as the coal approaches its yielding stage, the permeability can increase by up to 47%. (2) The permeability curve is divided into four regions: the compaction reduction zone, the oscillation zone, the gradual recovery zone, and the abrupt increase zone of failure. Ultimately, in the failure phase, the permeability surges dramatically, potentially reaching 4 to 5 times the initial permeability. (3) When the static load stage is constant, the rate of change in permeability of the coal under dynamic loading decreases with increasing adsorption amount. When the adsorption amount is constant, the rate of change in permeability of the coal under dynamic loading increases with the increase in static load stress stage, with the maximum increase reaching 75.2%. It can be concluded from the rate of change in permeability and the dynamic loading sensitivity coefficient that the permeability of the coal is highly sensitive to cyclic dynamic loading, with increased sensitivity associated with larger static load stages and decreased sensitivity with greater adsorption amounts.

The awareness of the vulnerability of existing structures under both seismic and energy viewpoints highlight the need to develop integrated retrofit solutions combining the structural and thermal performances. In this perspective, the study explores the efficacy of the Resisto 5.9 Tube system, a seismic coat that improves the seismic behaviour of masonry and reinforced concrete (RC) structures, also enhancing their energy performances, thanks to the thermal coat integrated in the basic steel system. The research involves the application of a design procedure specifically developed for this retrofit system from the seismic perspective. Once all the system elements are designed, non-linear static analyses are performed with a Finite Element software comparing the building’s seismic performances before and after the application of Resisto 5.9 Tube. The results demonstrate a significant increase in seismic safety coefficient, proving that this intervention type can be useful to obtain the seismic upgrading of existing RC buildings.

This study examines the attitudes and practices of dentists and dental students towards envi-ronmental issues within their professional context. Data was selected based on their responses on a Google Forms e-questionnaire, designed specifically for the study. The sample consisted of 78 professional dentists and 75 dental students in Greece and their attitudes towards environmental protection, awareness of the environmental impact of dental practices, and factors influencing the adoption of environmentally friendly practices were assessed. Results reveal generally positive attitudes towards environmental protection, with students demonstrating higher levels of proper waste management practices. Dentists express awareness of their environmental impact but ex-hibit varying levels of engagement in eco-friendly practices. Factors such as moral consciousness and believing in the severity of climate change significantly influence environmental awareness in dental practice. Notably, dental students exhibit greater environmental awareness compared to practicing dentists. These findings emphasize the importance of integrating environmental edu-cation into dental curricula and continuing education programs to foster sustainable practices in dental care. Future research could explore interventions to enhance environmental consciousness among practicing dentists and assess the long-term impact of environmental education initiatives on dental care sustainability.

Reconstructing the Tree of Life remains a central goal in biology. Early methods, relying on small numbers of morphological or genetic characters, often yielded conflicting evolutionary histories, undermining confidence in the results. Investigations based on phylogenomics, which use hundreds to thousands of loci for phylogenetic inquiry, have provided a clearer picture of life's history, but certain branches remain problematic. To resolve difficult nodes on the Tree of Life, two recent studies tested the utility of synteny, the conserved collinearity of orthologous genetic loci in two or more organisms, for phylogenetics. Synteny exhibits compelling phylogenomic potential while also raising new challenges. We identify and discuss specific opportunities and challenges that bear on the value of synteny data and other rare genomic changes for phylogenomic studies. Synteny-based analyses of highly contiguous genome assemblies mark a new chapter in the phylogenomic era and the quest to reconstruct the Tree of Life.

Background: wildfire modelers rely on Monte-Carlo simulations of wildland fire to produce burn-probability maps. These simulations are computationally expensive. Methods: we study the application of Importance Sampling to accelerating the estimation of burn probability maps, using L2 distance as the metric of deviation. Results: assuming a large area of interest, we prove that the optimal proposal distribution re-weights the probability of ignitions by the square root of expected burned area divided by expected computational cost, then generalize these results to assets-weighted L2 distance. We also propose a practical approach to searching for a good proposal distribution. Conclusion: these findings contribute quantitative methods for optimizing the precision/computation ratio of wildfire Monte Carlo simulations without biasing results, offer a principled conceptual framework for justifying and reasoning about other computational shortcuts, and can be readily generalized to a broader spectrum of simulation-based risk modeling.

Usage of novel non-tobacco oral nicotine delivery products (ONDP) has been increasing among adolescents in the United States. It is presumed that they are less toxic than their tobacco containing counterparts, but that has not been examined in controlled studies. Most of the studies have focused on non-renal manifestations of tobacco consumption via different means. The renal manifestations of non-tobacco ONDPs are not very well known, especially in immunocompromised patients. Here, we describe a 19-year-old adolescent with kidney transplant who sustained acute tubular necrosis leading to advanced chronic disease apparently following the usage of non-tobacco ONDP. Further larger studies are needed to study the renal manifestations of these next generation products to raise awareness among public.

Secure Secret Sharing Single-Source Shortest Distance (SSSD) protocols, built on the Secure Multi-Party Computation (SMC) foundation, have emerged as a promising solution to address the secure distribution and management of sensitive secrets among multiple parties. This paper focuses on the crucial aspect of security proof within SSSD protocols based on SMC, aiming to provide a scientifically rigorous understanding of the formal verification process and the robust guarantees they offer. Through a comprehensive examination of the fundamental principles underpinning SMC and the specific design considerations unique to SSSD protocols, this paper meticulously explores the key components and intricate steps involved in constructing a rigorous security proof. Furthermore, it discusses the adversarial model, precise security definitions, cryptographic assumptions utilized within the proof, and sophisticated techniques and reductions to establish its validity. By means of a meticulous analysis of the security proof for SSSD protocols, this paper critically evaluates both the strengths and limitations of the proposed approach, offering valuable insights to inform future research and development within this significant domain of study.

Startups often face uncertainty and the potential Valley of Death. To address these challenges, Sustainable Development Goals (SDGs) can serve as a starting point to identify business opportunities in markets driven by SDG-related issues, which is, in this case, using Virtual Reality (VR) technology as a “painkiller.” This study aims to explore VR use cases and their implications for startup business models and discover a problem-solution fit—defined as the degree to which VR applications effectively address actual problems as “killer applications.” The study employs a customized customer discovery technique, an essential process in the Customer Development methodology. By exploring VR's applicability in various domains such as entertainment, training, education, and tourism, this study unveils compelling use cases that align with SDG aspects, thus validating the hypothesis on startups' potential to harness VR for SDG contributions. Furthermore, the study produces VR business model designs based on solutions to SDG-related problems. Thus, startups can function effectively as “painkillers,” offering solutions that are not only desired but urgently needed (problem-solution fit). The implications of these findings extend globally, underlining VR's role in addressing SDG issues and offering practical insights for startup business model development that are applicable to broader technology adoptions.

In this paper, Rice water requirement and irrigation water requirement in Amol agro meteorological Station in 2016-2045 are forecasted based on the projected meteorological data of Hadcm3 under A2 scenario. Rice water requirements are estimated by using crop coefficient approach. Reference evapotranspiration are calculated by FAO Penman-Monteith method. Moreover, the irrigation water requirements are simulated by calibrated CROPWAT model using the meteorological parameters. The results show that both crop water requirement and irrigation water requirement present downward trend in the future. In 2016-2045, the rice water requirement and irrigation water requirement decrease by more than 9.9% under A2 scenario, respectively. Furthermore, the precipitation rise may be the main reason for the decrease in crop water requirement, while significant decrease of irrigation water requirement should be attributed to combined action of rising precipitation and a slight increase in temperature.

In the aerospace industry, the performance of materials on extreme conditions is crucial. This study addresses the development of nanoparticle-reinforced composite materials to improve thermal properties and flame retardancy in aerospace applications. As first approach, Zinc oxide (ZnO) nanoparticles were synthesized and incorporated into carbon fibers through the dip coating process. Subsequently, composite laminates were manufactured using the vacuum bag infusion method. The materials were subjected to flammability tests, following FAA regulations, to evaluate their performance against flame spread. The laminates reinforced with ZnO presented promise performances, suggesting a need for optimization in the incorporation of nanoparticles for safer and more efficient applications in the aerospace industry.

Based on the perspective of complex directed weighted network analysis, this paper constructs a static resilience evaluation framework for trade networks from two dimensions: structural resilience and node resilience. It studies the evolution of static resilience in the upstream, midstream, downstream, and recycling trade networks of global wood forest products from 2002 to 2021. The study finds that the total trade volume of global wood forest products has increased by 58.0%, 77.3%, 71.2%, and 77.2% in the upstream, midstream, downstream, and recycling sectors, respectively. The downstream trade network exhibits efficient information transmission and resource mobility capabilities. All four types of trade networks exhibit scale-free characteristics, with core countries such as China, the United States, and Germany playing a dominant role in the network and playing a crucial role in network robustness, but also increasing network vulnerability. In terms of unweighted assortativity, all four networks exhibit the characteristic of "small countries relying on hubs," while the weighted assortativity of the downstream network is positive, indicating enhanced network resilience. In terms of node resilience, countries exhibit significant differences and dynamic changes. China demonstrates strong resilience and adaptability, while the United States maintains its core position. China's influence in the downstream network has significantly increased, and India has rapidly emerged in the recycling network. To enhance network resilience, it is necessary to increase diversity and redundancy, establish effective emergency response mechanisms, and promote the diversification and green transformation of the supply chain.

Mitochondria are dynamic organelles that can adjust and respond to different stimuli within a cell. This plastic ability allows them to effectively coordinate several cellular functions in cells and becomes particularly relevant in highly complex cells such as neurons. An imbalance in mitochondrial dynamics can disrupt mitochondrial function, leading to abnormal cellular function and ultimately to a range of diseases, including neurodegenerative disorders. Regulation of mRNA transport and local translation inside neurons is crucial for maintaining the proteome of distal mitochondria, which is vital for energy production and synaptic function. A significant portion of the axonal transcriptome is dedicated to mRNAs for mitochondrial proteins, emphasizing the importance of local translation in sustaining mitochondrial function in areas far from the cell body. In neurons, local translation and the regulation of mRNAs encoding mitochondrial-shaping proteins could be essential for synaptic plasticity and neuronal health. The dynamics of these mRNAs, including their transport and local translation, may influence the morphology and function of mitochondria, thereby affecting the overall energy status and responsiveness of synapses. Comprehending the mitochondria-related mRNAs regulation and local translation, as well as its influence on mitochondrial morphology near the synapses will help to better understand neuronal physiology and neurological diseases where mitochondrial dysfunction and impaired synaptic plasticity play a central role.

Based on literature data, an analysis was conducted on the thermodynamic and kinetic dependencies of the thermal decomposition of methane. Course of this process was analysed for both thermal and thermal-catalytic conditions. The elementary reaction, i.e. the synthesis of methane from its elements, including its reversibility, was used as the basis for the for considerations on the kinetic model. The conversion degree was adopted as the kinetic parameter (kinetic variable) since this measure is essential for determining the amount of carbon deposit formed and thus closing the mass balance of this process. Several different kinetic models for the elementary reaction were analysed to determine how do they relate in regard to the constant of the reaction rate. It was determined that regardless of whether the process is catalytic or purely thermal, for temperatures t>900°C, the influence of reversibility of the reaction, has negligible influence on the yield of hydrogen produced, and thus can be omitted from the kinetic models. Special attention was given to iron-based catalysts, analysing the reactivity of Fe/C systems. It was demonstrated that the determined kinetic parameters satisfy the Kinetic Compensation Effect (KCE). Furthermore, by incorporating elements of Transition State Theory (TST), the existence of Entropy-Enthalpy Compensation (EEC) was highlighted. Based on these findings Authors show that through analysis of the changes in activation energy (E=20–421 kJ·mol-1) only an estimate of the optimal process conditions can be found, because the isoconversional temperature is also dependent on the way in which the reaction is conducted (T_{iso}=1200-1450K\ >\ \ T_{eq}). In other words, it was found that strong influences of such factors as T, P, etc. can locally compensate each other. Finally, it was also shown that the relations between KCE and EEC complement each other, which is confirmed by the determined average activation entropy at the isokinetic temperature (∆SB= -275.0 J·(mol·K)-1).

:. Background and Objectives: Pulmonary embolism, PE, incidence had been increasing in the last ten years. Computed thoracic pulmonary angiography, CTPA, had a major role in confirming PE di-agnosis.The main purpose of the study was to establish the prognostic value of a CTPA parameter: pulmonary artery obstruction index, PAOI, for PE risk assessment . A second objective of the re-search was to investigate the predictive accuracy of biomarkers (D-dimer, and cardiac Troponin T, c-TnT ), and PAOI, in 7-day mortality. The third objective of the study was the relationship between PAOI, and these biomarkers, in different etiologies of PE. Materials and Methods: The study com-prised 109 patients with PE, hospitalized, and treated between February 2021, and August 2022. They had different etiologies: deep vein thrombosis, DVT; persistent atrial fibrillation, AF; chronic obstructive pulmonary disease, COPD, exacerbation; Covid; and cancers. The investigations were the following: clinical examination; D-dimer testing, as a mandatory method for PE suspicion( values ≥ 500 µg/L were highly suggestive for PE); c-TnT , as a marker of myocardial injury(values ≥ 14 ng/L were abnormal); CTPA, with right ventricle dysfunction, RVD, signs, and PAOI. Treat-ments were according to PE risk: systemic thrombolysis (in high risk , and hemodynamic instability); low weight molecular heparins, LWMH, after systemic thrombolysis, or in high risk PE, with he-modynamic stability; direct oral anticoagulants, DOAC (in low, and intermediate risk). Results: The relationship PAOI- D-dimer, and PAOI- c-TnT, had very strong statistical correlation, for the entire sample, and for DVT, AF, COPD, Covid subgroups ( Rho = 0.815-0.982). PAOI had a high predic-tive accuracy for intermediate , and high risk together (area under curve, AUC=0.948), and high risk PE (AUC= 0.993). For 7-day mortality, D-dimer , and c-TnT had high predictive accura-cy(AUC=0.921, and 0.979). PAOI had statistical significance for 7-day mortality in Covid (p=0.045), and cancer (p=0.038) patients. Conclusions: PAOI was an important tool for PE risk as-sessment. D-dimer , and c-TnT were valuable predictors, for 7-day mortality in PE. PAOI, and D-dimer; PAOI, and c-TnT were strongly correlated, for the entire PE sample, and for DVT, AF, COPD, and Covid patients.

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

The Motile Aeromonad Septicemia (MAS) caused by Aeromonas species has been a serious problem in fish health management particularly in Nile tilapia (Orechromis niloticus). This study characterized an Aeromonas species isolated from farmed tilapia fingerlings in Binangonan, Rizal, Philippines, and tested for its pathogenicity in tank trials. We designated this isolate as Aeromonas veronii DFR01 (Diseased Fish Rizal) based on 16S rRNA phylogenetic analysis, 16S rRNA homology and MALDI-TOF MS identification with its biochemical profile generated from API and BiOLOG Gen III systems. A median lethal dose of A. veronii DFR01 was determined at 107 CFU/ml in tank trials and was utilized as whole cell inactivated antigen for oral vaccine development. The immunized tilapia fingerlings produced elevated levels of IgM in the blood as determined by enzyme-linked immunosorbent assay (ELISA). The increase in IgM levels is significantly higher 14 days post vaccination. The relative IgM gene expression as determined by quantitative polymerase chain reaction (qPCR) showed increasing levels after vaccination until 38 days of culture. The vaccinated fish showed 25-35% cumulative mortalities compared to the non-vaccinated-challenged group. Results showing improved relative percent survival (RPS) rate in tilapia fingerlings and induced immune response after vaccination indicate the potential use of the oral vaccine in cultured tilapia.

Objective To investigate the functional and biological significance of telomere-related active genes in cholangiocarcinoma (CCA) using single-cell sequencing and cell communication analysis. Methods First, we identified and annotated different cell types in CCA samples using single-cell sequencing data. Pseudotime analysis revealed the transcriptional status and differentiation process of CCA cells. We then explored interactions between CCA cells and their regulatory pathways through cell communication analysis. 47 key genes related to telomere activity were identified, and GO and KEGG pathway enrichment analysis was performed. Additionally, we constructed a prognostic model and verified the model's predictive efficacy. Finally, we identified genes associated with drug sensitivity in CCA patients and identified pathways associated with CCA prognosis through tumor TMB analysis. Results We identified 452 active telomere-associated cells. Then, we assessed telomere associated gene activity, obtaining 47 differentially expressed telomere genes by intersecting with CCA differentially expressed genes. Major up-regulated signaling pathways, including MIF-(CD74+CXCR4) and HLA-A-CD8A, were identified through pseudotime analysis. Three telomere-associated prognostic genes were identified using LASSO analysis, and prognostic-related models were built. In addition, PBRM1 mutation load was found to be highest in the high-risk group, making it a potential diagnostic target. Finally, drug sensitivity analysis revealed higher sensitivity to certain therapeutic drugs in the high-risk group, providing guidance for tailored CCA treatment. Conclusion The MIF-(CD74+CXCR4) and HLA-A-CD8A signaling pathways appear to be major pathways for CCA cell proliferation. Prognostic models based on telomere-associated genes show promise for application, indicating the biological and clinical significance of telomere activity genes in CCA.

Delineating chilling and forcing periods is one of the challenging topics in understanding how temperatures drive the timing of budburst and bloom in fruit tree species. Here, we investigated this question on olive trees, using flowering data collected over six years on 331 cultivars in the worldwide collection of Marrakech, Morocco. Using a Partial Least Squares approach on a long-term phenology (29 years) of ‘Picholine Marocaine’ cultivar, we showed that the relevance of delineating the chilling and forcing periods depends more on the variability of inter-annual temperatures than on the long-term datasets. In fact, chilling and forcing periods are similar between those delineated by using datasets of 29 years and those of only 6 years (2014–2019). We demonstrated that the variability of inter-annual temperatures is the main factor explaining this pattern. We then used the datasets of six years to assess the chill and heat requirements of 285 cultivars. We classified Mediterranean olive cultivars into four groups according to their chill requirements. Our results, using the Kriging interpolation method, indicated that flowering dates of most of these cultivars (92%) were governed by both chilling and forcing temperatures. Our investigations provided first insights to select adapted cultivars to global warming.

The Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. In the current climate change scenario and where cropland is becoming a limited resource, there is a growing need for a more efficient land use and occupation. It is estimated that more than half of the Brazilian pastures have some level of degradation; however there is still no mapping of the quality of pastures on a national scale. In this study, we mapped and evaluated the spatio-temporal dynamics of pasture quality in Brazil, between 2010 and 2018, considering three classes of degradation: Absent (D0), Intermediate (D1), and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, in spite of the accentuated spatial dynamics, with a retraction in the center-south and expansion to the north, over areas of ​​native vegetation. The percentage of non-degraded pastures increased ~12%, due to the recovery of degraded areas and the emergence of new pasture areas as a result of the prevailing spatial dynamics. However, about 44 Mha of the pasture area is currently severely degraded. The dynamics in pasture quality were not homogeneous in property size classes. We observed that in the approximately 2.68 million properties with livestock activity, the proportion with quality gains was twice as low in small properties compared to large ones, and the proportion with losses was three times greater, showing an increase in inequality between properties with more and less resources (large and small, respectively). The areas occupied by pastures in Brazil present an unique opportunity to increase livestock production and make available areas for agriculture, without the need for new deforestation in the coming decades.

The present study is reviewing leachate models useful for proactive and rehab actions to safeguard surface and subsurface soft water which become even more scarce. Integrated management plans of water basins are of crucial importance since intensively cultivated areas are adding huge quantities of fertilizers affecting surface water basins and groundwater. Aquifers progressively are being nitrified on account of the nitrogen-based fertilizers’ surplus, rendering water for human consumption not potable. Well-tested solute leaching models standalone or part of a model package provide rapid site-specific estimates of the leaching potential of chemical agents, mostly nitrates, below root zone of crops and the impact of leaching towards groundwater. Most of the models examined were process-based or conceptual approach. Nonetheless, empirical prediction models, though rather simplistic and therefore not preferrable, demonstrate certain advantages such as extensive calibration database information requirements, which in many cases is unavailable not to mention stochastic approach and the involving of Artificial Intelligence (AI). Models were categorized according to the porous medium and agents to be monitored. Integrated packages of nutrients’ models are irreplaceable element for extensive catchment to monitor the terrestrial nitrogen balanced cycle and to contribute to the policy making as regards the soft water management.

The concept and practice of ’livelihood’ has undergone various stages in the development of Western countries. Its core has always focused on eradicating rural poverty and promoting rural development. This study utilizes the entropy value method to measure the comprehensive development level of the livelihood sustainability of farm households. Additionally, the Theil index is used to measure the presentation income gap, and multiple linear regression is utilized to test the impact of the livelihood capital sustainability of farm households on the presentation income gap. Results showed that the the sustainability of farm households’ livelihood capital has a significant negative impact on the urban-rural income gap. In other words, improving the livelihood capital of farm households can significantly reduce the urban-rural income gap. Accordingly, it is proposed that efforts be made to enhance the sustainability of farm households’ livelihoods, and to increase the contribution of the sustainable enhancement of farm households’ livelihood capital to narrowing the rural-urban income gap.

Amuyo Ponds (AP) are a group of three hydrothermal lagoons located at 3,700 m above sea level, in a pre-Andean setting at the Atacama Desert. Each pond shows a conspicuous green, red, or yellow coloration, and discharge waters rich in arsenic and boron into the Caritaya River (Camarones Basin, Arica and Parinacota Region, in northern Chile). Microorganisms at these ponds are subjected to harsh environmental conditions, and microbial composition and diversity in Amuyo Ponds sediments are unknown. Then, microbial life colonizing AP sediments was explored by metagenomics analyses. This approach showed a highly diverse microbial life dominated by members of the Bacterial Domain (nearly 800 bacterial genome sequences) and includes sequences associated to Archaea, Eukarya, and viruses. We also report putative functional capabilities related to virulence and defense genes, and biosynthesis of secondary metabolites. Thirteen bacterial and fourteen virus metagenome-assembled genomes were reconstructed and are here informed. This work expands our knowledge on the richness of microorganisms in AP and open further studies on the ecology and genomics of this striking Andean geosite.