This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera’s hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat’s ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency’s Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat’s suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications. Full article

Autism Spectrum Disorder (ASD) is an early onset neurodevelopmental disorder characterized by impaired social interaction and communication, and repetitive patterns of behavior. Family studies show that ASD is highly heritable, and hundreds of genes have previously been implicated in the disorder; however, the etiology is still not fully clear. Brain imaging and electroencephalography (EEG) are key techniques that study alterations in brain structure and function. Combined with genetic analysis, these techniques have the potential to help in the clarification of the neurobiological mechanisms contributing to ASD and help in defining novel therapeutic targets. To further understand what is known today regarding the impact of genetic variants in the brain alterations observed in individuals with ASD, a systematic review was carried out using Pubmed and EBSCO databases and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review shows that specific genetic variants and altered patterns of gene expression in individuals with ASD may have an effect on brain circuits associated with face processing and social cognition, and contribute to excitation–inhibition imbalances and to anomalies in brain volumes. Full article

Malachite is one of the most important copper-bearing oxide minerals; however, it shows poor floatability prior to sulfidization under the thiol collector system. This study investigated the reasons for the low recovery of malachite flotation without sulfidization. The results of adsorption capacity and contact angle test indicated that the malachite surface could adsorb a sufficient amount of the collector, obviously increasing the hydrophobicity of the malachite surface under static conditions. By measuring the amount of inorganic carbon in the flotation solution, it was found that the amount of inorganic carbon in the solution increased significantly when the thiol collectors were added into pulp, which could be attributed to the induced dissolution of the malachite surface by thiol collectors. Solubility tests further demonstrated that the copper ions released from the natural dissolution of malachite proved difficult in regard to reactions with thiol collector to form precipitates; however, the thiol collector induced the dissolution of malachite surface, and so the hydrophobic complexes’ copper-collector could not firmly adsorb on the mineral surface. Fourier transform infrared (FTIR) analysis revealed that thiol collectors do not adsorb stably on malachite surfaces. This was considered to be a substantial reason for the poor performance of malachite flotation without sulfidization. Full article

A reasonable allocation of production schedules and savings in overall electricity costs are crucial for large manufacturing conglomerates. In this study, we develop an optimization model of off-site industrial production scheduling to address the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China’s power supply, and the difficulty in promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and variations in electricity prices in different places to optimize the scheduling of industrial production in various locations. The model is embedded with two sub-models, i.e., an electricity price prediction model and a distributed photovoltaic power cost model to complete the model parameters, in which the electricity price prediction model utilizes a Long Short-Term Memory (LSTM) neural network. Then, the particle swarm optimization algorithm is used to solve the optimization model. Finally, the production data of two off-site pharmaceutical factories belonging to the same large group of enterprises are substituted into the model for example analysis, and it is concluded that the optimization model can significantly reduce the electricity consumption costs of the enterprises by about 7.9%. This verifies the effectiveness of the optimization model established in this paper in reducing the cost of electricity consumption on the demand side. Full article

Somatic cell nuclear transfer (SCNT) technology holds great promise for livestock industry, life science and human biomedicine. However, the development and application of this technology is limited by the low developmental potential of SCNT embryos. The developmental competence of cloned embryos is influenced by the energy metabolic status of donor cells. The purpose of this study was to investigate the effects of CPI, an oxidative phosphorylation inhibitor, on the energy metabolism pathways of pig fibroblasts and the development of subsequent SCNT embryos. The results showed that treatment of porcine fibroblasts with CPI changed the cellular energy metabolic pathways from oxidative phosphorylation to glycolysis and enhanced the developmental ability of subsequent SCNT embryos. The present study establishes a simple, new way to improve pig cloning efficiency, helping to promote the development and application of pig SCNT technology. Full article

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst’s excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process’s appeal is further enhanced by its cyclic nature—the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis. Full article

The controlled manufacturing of semiconductor photocatalysts is crucial to their development for drinking water treatment. In this study, TiO₂-coated meltblown nonwoven fabrics prepared via Atomic Layer Deposition (ALD) are applied for the inactivation of Escherichia coli (E. coli). It is observed that in the presence of an ultraviolet light-emitting diode (UV-LED) light source (255 nm), 1.35 log E. coli inactivation is achieved. However, exposure to catalyst-coated fabrics in addition to the light source resulted in >4 log E. coli inactivation, suggesting a much higher rate of hydroxyl radical formation on the surface, leading to cell death. Full article

Green algae blooms of the genus Ulva are occurring globally and are primarily attributed to anthropogenic factors. At Los Tubos beach in Algarrobo Bay along the central Chilean coast, there have been blooms of these algae that persist almost year-round over the past 20 years, leading to environmental, economic, and social issues that affect the local government and communities. The objective of this study was to characterize the species that form these green tides based on a combination of ecological, morpho-anatomical, and molecular information. For this purpose, seasonal surveys of beached algal fronds were conducted between 2021 and 2022. Subsequently, the sampled algae were analyzed morphologically and phylogenetically using the molecular markers ITS1 and tufA, allowing for the identification of at least five taxa. Of these five taxa, three (U. stenophylloides, U. uncialis, U. australis) have laminar, foliose, and distromatic morphology, while the other two (U. compressa, U. aragoensis) have tubular, filamentous, and monostromatic fronds. Intertidal surveys showed that U. stenophylloides showed the highest relative coverage throughout the seasons and all intertidal levels, followed by U. uncialis. Therefore, we can establish that the green tides on the coast of Algarrobo in Chile are multispecific, with differences in relative abundance during different seasons and across the intertidal zone, opening opportunities for diverse future studies, ranging from ecology to algal biotechnology. Full article

Neutral-temperature district heating and cooling (NT-DHC) is a recent concept in the district heating sector. The current literature does not directly address the ability to create comprehensive master plans for NT-DHC systems and reliably model their performance. This research presents a new approach for the evaluation and planning of NT-DHC systems. The methodology involves the use of a knapsack optimization algorithm to perform a comprehensive analysis of the conditions that make the NT-DHC solution competitive against individual heating and cooling technologies. The algorithm determines the optimal combination of potential extensions that maximizes overall economic value. The results of a case study, which was conducted in Italy, show that NT-DHC is more suitable in dense urban areas, while air-to-water heat pumps are better suited for low heat density zones. This methodology aims to reduce the risks associated with energy demand and provide more certainty about which areas a network can expand into to be competitive. It is targeted at energy planners, utilities experts, energy engineers, and district heating experts who require assistance and guidance in the planning and early stages of designing a NT-DHC system. This method might enable pre-feasibility studies and preliminary design to determine the opportunities and limitations of a system of this kind from an economic and technological perspective. Full article

In this research paper, we investigate the controllability in the $\alpha$-norm of a coupled system of integrodifferential equations with state-dependent nonlocal conditions in generalized Banach spaces. We establish sufficient conditions for the system’s controllability using resolvent operator theory introduced by Grimmer, fractional power operators, and fixed-point theorems associated with generalized measures of noncompactness for condensing operators in vector Banach spaces. Finally, we present an application example to validate the proposed methodology in this research. Full article

Pavement performance models (PPMs) are utilized to predict pavement network conditions which is an essential part of any sustainable pavement management system (PMS). The reliability of a PMS and its outputs is proportional to the reliability of the PPM used. This article describes a mechanistic–empirical pavement performance model based on pavement response parameters—strains calculated in the pavement layers measured by tensometers embedded in the pavement surface and verified by calculations in the elastic half-space model and supplemented by empirical data from long-term pavement performance monitoring and accelerated pavement testing. Hence, the herein described PPM combines pavement serviceability evaluation, pavement bearing capacity, and the physico-mechanistic properties of paving materials. The analytical methods which were used to ascertain the physico-mechanistic characteristics, the material fatigue degradation model, and the surface degradation, unevenness in particular, are described. A comparison of the empirical PPM created in the last century used by the national road administrator to this day and the newly created PPM is presented. The comparison shows the difference in the calculated socio-economic benefits and subsequent cost–benefit analysis results. The comparison shows that the use of the old PPM may have produced false economic evaluation results that have led to poor decision making, partially explaining the unsustainable trend of road network management in our country. Full article

In Li Bai’s poems, the term yao or medicine is frequently employed as an idea-image. The meaning of yao can be further divided into four distinct types, each corresponding to its functions in different contexts. It represents the elixir found on Penglai Island, having the power to elevate a person to immortality; the elixir stolen from the Queen Mother of the West by Heng’E; the immortal herbs pounded by the Jade Rabbit; and the medicine used for treating diseases. In addition, Li Bai’s poems also contain elixir liquid (danye 丹液), potable gold (jinye 金液), and other substances referred to as yao. Unlike specific terms like “cinnabar,” these names are more general in nature. The medicines, their names, and the general terms in poems carry different emotional implications, e.g., his admiration for immortality, and a means to criticize his own time, to express his aspirations and lamentation over the passage of time. The “Yao” also serves as a symbol of healing and nourishment, especially in the context of friendship. All these points deserve to be meticulously explored. Full article

Planar reconstruction detects planar segments and deduces their 3D planar parameters (normals and offsets) from the input image; this has significant potential in the fields of digital preservation of cultural heritage, architectural design, robot navigation, intelligent transportation, and security monitoring. Existing methods mainly employ multiple-view images with limited overlap for reconstruction but lack the utilization of the relative position and rotation information between the images. To fill this gap, this paper uses two views and their relative camera pose to reconstruct indoor scene planar surfaces. Firstly, we detect plane segments with their 3D planar parameters and appearance embedding features using PlaneRCNN. Then, we transform the plane segments into a global coordinate frame using the relative camera transformation and find matched planes using the assignment algorithm. Finally, matched planes are merged by tackling a nonlinear optimization problem with a trust-region reflective minimizer. An experiment on the Matterport3D dataset demonstrates that the proposed method achieves 40.67% average precision of plane reconstruction, which is an improvement of roughly 3% over Sparse Planes, and it improves the IPAA-80 metric by 10% to 65.7%. This study can provide methodological support for 3D sensing and scene reconstruction in sparse view contexts. Full article

Chagas disease is caused by the single-flagellated protozoan Trypanosoma cruzi, which affects several million people worldwide. Understanding the signal transduction pathways involved in this parasite’s growth, adaptation, and differentiation is crucial. Understanding the basic mechanisms of signal transduction in T. cruzi could help to develop new drugs to treat the disease caused by these protozoa. In the present work, we have demonstrated that Fetal Calf Serum (FCS) can quickly increase the levels of both phosphorylated and unphosphorylated forms of T. cruzi DNA polymerase beta (TcPolβ) in tissue-cultured trypomastigotes. The in vitro phosphorylation sites on TcPolβ by protein kinases TcCK1, TcCK2, TcAUK1, and TcPKC1 have been identified by Mass Spectrometry (MS) analysis and with antibodies against phosphor Ser-Thr-Tyr. MS analysis indicated that these protein kinases can phosphorylate Ser and Thr residues on several sites on TcPolβ. Unexpectedly, it was found that TcCK1 and TcPKC1 can phosphorylate a different Tyr residue on TcPolβ. By using a specific anti-phosphor Tyr monoclonal antibody, it was determined that TcCK1 can be in vitro autophosphorylated on Tyr residues. In vitro and in vivo studies showed that phorbol 12-myristate 13-acetate (PMA) can activate the PKC to stimulate the TcPolβ phosphorylation and enzymatic activity in T. cruzi epimastigotes. Full article

Chronic pancreatitis (CP) is a rare but debilitating condition with an 8-fold increased risk of developing pancreatic cancer. In addition to the symptoms that come from the loss of endocrine and exocrine function in CP, the management of chronic pain is problematic. We previously showed that the CCK-receptor antagonist called proglumide could decrease inflammation, acinar-ductal metaplasia, and fibrosis in murine models of CP. We hypothesized that proglumide would be safe and diminish pain caused by CP. A Phase 1 open-labeled safety study was performed in subjects with clinical and radiographic evidence of CP with moderate to severe pain. After a 4-week observation period, the subjects were treated with proglumide in 400 mg capsules three times daily (1200 mg per day) by mouth for 12 weeks, followed by 4 weeks of observation after discontinuation for safety. The results of three pain surveys (Numeric Rating Scale, COMPAT-SF, and NIH PROMIS) showed that the patients had significantly less pain after 12 weeks of proglumide compared to the pre-treatment observation phase. Of the eight subjects in this study, two experienced nausea and diarrhea with proglumide. These side effects resolved in one subject with doses reduced to 800 mg per day. No abnormalities were noted in the blood chemistries. A blood microRNA blood biomarker panel that corresponded to pancreatic inflammation and fibrosis showed significant improvement. We conclude that proglumide is safe and well tolerated in most subjects with CP at a dose of 1200 mg per day. Furthermore, proglumide therapy may have a beneficial effect by decreasing pain associated with CP. Full article

Osteoporosis constitutes a significant public health concern necessitating proactive prevention, treatment, and monitoring efforts. Timely identification holds paramount importance in averting fractures and alleviating the overall disease burden. The realm of osteoporosis diagnosis has witnessed a surge in interest in machine learning applications. This burgeoning technology excels at recognizing patterns and forecasting the onset of osteoporosis, paving the way for more efficacious preventive and therapeutic interventions. Smart walkers emerge as valuable tools in this context, serving as data acquisition platforms for datasets tailored to machine learning techniques. These datasets, trained to discern patterns indicative of osteoporosis, play a pivotal role in enhancing diagnostic accuracy. In this study, encompassing 40 participants—20 exhibiting robust health and 20 diagnosed with osteoporosis—data from force sensors embedded in the handlebars of conventional walkers were gathered. A windowing action was used to increase the size of the dataset. The data were normalized, and k-fold cross-validation was applied to assess how well our model performs on untrained data. We used multiple machine learning algorithms to create an accurate model for automatic monitoring of users’ gait, with the Random Forest classifier performing the best with 95.40% accuracy. To achieve the best classification accuracy on the validation dataset, the hyperparameters of the Random Forest classifier were further adjusted on the training data. The results suggest that machine learning-based automatic monitoring of gait parameters could lead to accurate, non-laborious, cost-effective, and efficient diagnostic tools for osteoporosis and other musculoskeletal disorders. Further research is needed to validate these findings. Full article

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation. Full article

A device called a heat exchanger is used to exchange heat transfer between two fluids with different temperatures. Because of its durability and ability to handle high-pressure application, the concentric double pipe heat exchangers are widely utilized for numerous industrial applications. To conserve pumping power energy, many researchers were involved in study of the nanoparticles to be embedded in the fluid, which will enrich the fluid thermal conductivity and surface area. This article demonstrates the flow characteristics and convective heat transfer of nanofluids containing 0.2, 0.4 and 0.6 of vol% TiO₂ nanoparticles dispersed in water under turbulent conditions, which mainly can be used for cooling nuclear reactors applications. Reynolds numbers varying from 4000 to 18,000 are examined numerically. The convective heat transfer coefficient results of the nanofluid agree well against experimental data, which are slightly more than that of base water at 1.94%. The results of the numerical model showed that the convective heat transfer coefficient of nanofluids will increase when the Reynolds and volume fraction increases. By increasing the temperature of the annular hot water, the heat transfer rate will increase, showing no major impact to the convective heat transfer coefficient of nanofluids. A generalised solution predicting the convective heat transfer coefficient for extensive nanoparticle materials is proposed. The conclusion of the empirical equation is tested among published data and the results are highly congruent, confirming the strength of the gamma equation. Full article

Berberis microphylla, commonly known as calafate, is one of the most promising species of Chilean Patagonia to be domesticated, due to its anthocyanin-rich berries. The main aim of this study was to understand the genetic structure of the wild populations of B. microphylla in the main regions where it grows and its relationship with phenolic secondary metabolite profiles. Ripe berry samples and leaves were collected from the Aysén and Magallanes regions. Genetic analyses were carried out using 18 microsatellite markers. Phenolic compounds were extracted from the ripe fruits and were quantified using high-performance liquid chromatography (HPLC). Their antioxidant capacity was determined according to the Trolox equivalent antioxidant capacity (TEAC) assay. Total phenols were measured as their absorbance at a wavelength of 280 nm. Both the genetic and chemometric data were explored using unsupervised and supervised methods. The genetic markers suggest the existence of three groups, two of them corresponding to the Aysén and Magallanes samples, and the third corresponding to Chile Chico (a district in Aysén), which was the most divergent of the three. Similar results were observed in the phenolic profile obtained with chemometric analysis, with the same samples forming a separate third group. The differentiation achieved using the genetic and chemical data may be the result of intrinsic genetic differences, environmental effects on fruit maturity, or the sum of both factors. These are all points to consider in the domestication of this valuable species by selecting individuals with desirable traits and contrasting phenotypes. Full article