The control of irrigation volume is of significant importance in arid regions of northwest China. Particularly, it has a crucial impact on the salinization of shallow groundwater areas. In 2022 and 2023, field experiments were conducted to test three distinct under-membrane irrigation treatments. These treatments were assigned water quotas of HW (27 mm), MW (22.5 mm), and LW (18 mm). The HYDRUS-2D model was integrated with a field experiment to accurately simulate the dynamic fluctuations of soil water and salt in the sunflower root zone. The model’s performance was assessed and verified using real-field data from 2022 and 2023, and the simulation results closely matched the measured values. This research also used stable hydroxide isotopes to assess the water supply from various soil layers at different time intervals in sunflower plants. The results indicated that the three different levels of irrigation applied under the membrane had a significant impact on soil water content. Specifically, there was a significant difference in soil water content at a depth of 0–40 cm (p < 0.05), while there was little effect on the water content at a depth of 40–60 cm (p > 0.05). After irrigation, the average salt content in the top 0–20 cm of soil decreased by 7.0% compared to the medium and low irrigation levels, and by 10.8% compared to the medium irrigation level. Additionally, the medium irrigation level resulted in a 10.8% decrease in salt content compared to the low irrigation level, and a 4.1% decrease compared to the medium irrigation level. During the same period, the soil salinity levels at depths of 0–20 cm, 20–40 cm, 40–60 cm, and 60–100 cm in the area outside the membrane were measured to be 2.7~4.8 g·kg⁻¹, 2.8~4.0 g·kg⁻¹, 2.7~3.4 g·kg⁻¹, and 1.7~2.6 g·kg⁻¹, respectively. These levels decreased by 13.1~55.5%, 0.7~42.8%, −0.4~16.2%, and −72.7~7.5%, respectively. Following irrigation, the HW treatment mostly absorbed water in the 0–40 cm soil layer, while the MW and LW treatments absorbed water in both the 0–40 cm and 60–80 cm soil levels. The results indicated that the most optimal drip irrigation method beneath the membrane in this location was achieved when the amount of water applied was between 25–30 mm. This method demonstrated a combination of water conservation, high crop yield, and effective salt suppression. Full article

Texture, soil organic matter (SOM), and soil depth (SoD) are crucial properties in forest management because they can supply spatial information on forest site productivity and guide fertilizer applications. However, soil properties possess an inherent uncertainty that must be mapped to enhance decision making in management applications. Most digital soil mapping predictions primarily concentrate on the mean of the distribution, often neglecting the estimation of local uncertainty in soil properties. Additionally, there is a noticeable scarcity of practical soil examples to demonstrate the prediction uncertainty for the benefit of forest managers. In this study, following a digital soil mapping (DSM) approach, a Quantile Regression Forest (QRF) model was developed to generate high-resolution maps and their uncertainty regarding the texture, SoD, and SOM, which were expressed as standard deviation (Sd) values. The results showed that the SOM (R² = 0.61, RMSE = 2.03% and with an average Sd = 50%), SoD (R² = 0.74 and RMSE = 19.4 cm), clay (R² = 0.63, RMSE = 10.5% and average Sd = 29%), silt (R² = 0.59, RMSE = 6.26% and average Sd = 33%), and sand content (R² = 0.55, RMSE = 9.49% and average Sd = 35%) were accurately estimated for forest plantations in central south Chile. A practical demonstration of precision fertilizer application, utilizing the predictive distribution of SOM, effectively showcased how uncertainty in soil attributes can be leveraged to benefit forest managers. This approach holds potential for optimizing resource allocation and maximizing economic benefits. Full article

The objective of this study was to investigate the upper incisors (U1), lower incisors (L1), and soft tissue profiles of bimaxillary protrusion (BM) adult patients among skeletal Class I (BM1), II (BM2) and III (BM3). Understanding these characteristics would be useful for incisor and lip diagnostics in different skeletal classifications. Fourteen linear and twelve angular variables of the incisors and lips were evaluated in 214 lateral cephalograms (BM1 = 91, BM2 = 84, BM3 = 39). ANOVA and Bonferroni tests compared the measurements. BM1 and BM3 exhibited a greater U1 position and U1 inclination than the norms, while BM2 presented only a greater U1 position than the norms but normal U1 inclination. BM1 and BM3 had a significantly greater U1 position than BM2. BM1 and BM2 demonstrated a greater L1 position and L1 inclination than the norms, whereas BM3 displayed only a greater L1 position than the norms but normal L1 inclination. BM2 had the most anterior L1 position, whereas BM3 had the least anterior position. Only BM2 had a longer anterior dental height (ADH) than the norms, while BM1 and BM3 had a normal ADH and the significantly shortest ADH, respectively. Only BM1 had a normal upper incisor display at rest (U1R), while BM2 and BM3 displayed an increased and decreased U1R, respectively, with significant differences among the three groups. The most significantly protruded upper and lower lips were presented in BM2, but these were exhibited the least in BM3. The most significant acute nasolabial angle (NLA) was found in BM3, whereas BM2 presented the least acute NLA. A normal lip–chin–throat angle (LCTA) was observed in BM1 and BM3, while only BM2 had a greater LCTA than the norms. The most significant obtuse LCTA was found in BM2, while BM3 had the least obtuse LCTA. Therefore, both U1 and L1 in all groups presented protrusion and proclination, except for U1 in BM2, while L1 in BM3 exhibited normal inclination. The ADH and U1R were increased in BM2 but decreased in BM3. The most acute NLA was found in BM3, whereas the least acute was found in BM2. The most obtuse LCTA was in BM2, while the least was in BM3. Full article

Liquid films are often found in engineering applications with thicknesses ranging from micrometer scales to large scales with a wide range of applications. To optimize such systems, researchers have dedicated themselves to the development of new techniques. To further contribute to this development, the objective of this work is to present the results of the collision of water droplets on a wall by means of experimentation and numerical simulations. For physical experimentation, an injector is used to generate a chain of water droplets that collide with the opposite wall, forming a liquid film. Images of the droplets were obtained using two high-speed recording cameras. The results for different droplet sizes and impact angles are presented and the relationship between the momentum parameter and non-dimensional pool size was established. Modeling such processes is a common challenge in engineering, with different techniques having their advantages and limitations. The simulations in this work were run using the volume of fluid method, which consists of solving a transport equation for the volume fraction of each considered fluid. A correlation was found between the surface tension to momentum transport ratio, Scd, and the non-dimensional pool size for different droplet sizes and impact angles. Regions where partial depositions were most likely to occur were found via physical experiments. Full article

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model’s understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities. Full article

Non-specific low back pain (NSLBP) in children and adolescents has increased in recent years, and the evidence of the physiotherapy interventions in back care needs to be updated. Our main goal was to quantify the effects of preventive physiotherapy interventions on improving behavior and knowledge related to back care and prevention of NSLBP in children and adolescents. Based on two previous meta-analyses, Cochrane Library, MEDLINE, PEDro, Web of Science, LILACS, IBECS, PsycINFO, and IME databases and several journals were searched. Two researchers independently extracted data and assessed the risk of bias in the studies using the RoB2 tool. Data were described according to PRISMA guidelines. A total of 24 studies (28 reports) were included. In the posttest, the behavior variable obtained an overall effect size of d₊ = 1.48 (95%CI: 0.40 to 2.56), and the knowledge variable obtained an effect size of d₊ = 1.41 (95%CI: 1.05 to 1.76). Physiotherapy has demonstrated beneficial impacts on behavior and knowledge concerning back care and to prevent NSLBP in children and adolescents. Interventions focusing on postural hygiene and exercise should be preferred, especially those that are shorter in number of weeks, more intense, and incorporate as many intervention hours as possible. Full article

Background: Long bones are commonly affected by musculoskeletal tumors, but they also represent one of the most frequent locations for metastases. The treatment is based on pain management and the prevention or stabilization of pathological fractures by intramedullary nailing. While titanium nails are probably the most used, carbon-fiber-reinforced (CFR) nails have emerged as a new option for oncological patients. The aim of this review is to compare titanium and CFR nails according to current findings. Methods: Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) standards were followed: a total of 1004 articles were identified and 10 were included. Results: Traditionally, titanium implants are highly valued for their optimal biomechanical properties and ease of insertion, facilitated by their radiopacity. However, the use of titanium poses challenges in radiotherapy due to interference with radiation dosage and the creation of ferromagnetic artifacts. Conversely, CFR implants have emerged as a recommended option for intramedullary fixation, due to their biomechanical and structural properties and their benefits during radiotherapy and follow-up monitoring X-ray. Conclusions: CFR nailing represents a promising advancement in the surgical management of oncological patients with long bone metastases. However, further studies are needed to increase surgeons’ confidence in their use. Full article

The aim of this study was to evaluate the effect of different irrigating solutions as well as their combination and activation modes on root canal dentin microhardness. The protocol was registered in PROSPERO and PRISMA guidelines were followed. The structured question was as follows: “Which type of irrigating solution used in endodontic treatment causes more change in dentin microhardness?” The literature was screened via PubMed, Google Scholar, Scopus, and Science Direct. The last search was carried out in February 2023 with English language restriction. Two reviewers independently performed screening and evaluation of articles. A total of 470 articles were retrieved from all the databases, whereas only 114 articles were selected for full-text analysis. After applying eligibility criteria, 44 studies were evaluated and included in this review. The results showed that with increased contact time with irrigants, dentin microhardness decreases. Increased contact time with sodium hypochlorite (NaOCl) was associated with more reduction in dentin microhardness compared with other irrigants. Other irrigants, with the exception of distilled water, including EDTA, citric acid, herbal irrigants, glycolic acid, phytic acid, etc., in this study significantly decreased dentin microhardness. The maximum reduction in dentin microhardness was seen with 2.5% NaOCl after 15 min of contact time. The use of irrigating solutions alters the chemical composition of dentin, thereby decreasing its microhardness, which affects the clinical performance of endodontically treated teeth. Full article

Reduced graphene oxide (rGO) and a proteasome inhibitor (MG-132) are some of the most commonly used compounds in various biomedical applications. However, the mechanisms of rGO- and MG-132-induced cytotoxicity remain unclear. The aim of this study was to investigate the anticancer effect of rGO and MG-132 against ZR-75-1 and MDA-MB-231 breast cancer cell lines. The results demonstrated that rGO, MG-132 or a mix (rGO + MG-132) induced time- and dose-dependent cytotoxicity in ZR-75-1 and MDA-MB-231 cells. Apart from that, we found that treatment with rGO and MG-132 or the mix increased apoptosis, necrosis and induction of caspase-8 and caspase-9 activity in both breast cancer cell lines. Apoptosis and caspase activation were accompanied by changes in the ultrastructure of mitochondria in ZR-75-1 and MDA-MB-231 cells incubated with rGO. Additionally, in the analyzed cells, we observed the induction of oxidative stress, accompanied by increased apoptosis and cell necrosis. In conclusion, oxidative stress induces apoptosis in the tested cells. At the same time, both mitochondrial and receptor apoptosis pathways are activated. These studies provided new information on the molecular mechanisms of apoptosis in the ZR-75-1 and MDA-MB-231 breast cancer cell lines. Full article

In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing. Full article

Harnessing solar energy to produce hydrogen through semiconductor-mediated photocatalytic water splitting is a promising avenue to address the challenges of energy scarcity and environmental degradation. Ever since Fujishima and Honda’s groundbreaking work in photocatalytic water splitting, titanium dioxide (TiO₂) has garnered significant interest as a semiconductor photocatalyst, prized for its non-toxicity, affordability, superior photocatalytic activity, and robust chemical stability. Nonetheless, the efficacy of solar energy conversion is hampered by TiO₂’s wide bandgap and the swift recombination of photogenerated carriers. In pursuit of enhancing TiO₂’s photocatalytic prowess, a panoply of modification techniques has been explored over recent years. This work provides an extensive review of the strategies employed to augment TiO₂’s performance in photocatalytic hydrogen production, with a special emphasis on foreign dopant incorporation. Firstly, we delve into metal doping as a key tactic to boost TiO₂’s capacity for efficient hydrogen generation via water splitting. We elaborate on the premise that metal doping introduces discrete energy states within TiO₂’s bandgap, thereby elevating its visible light photocatalytic activity. Following that, we evaluate the role of metal nanoparticles in modifying TiO₂, hailed as one of the most effective strategies. Metal nanoparticles, serving as both photosensitizers and co-catalysts, display a pronounced affinity for visible light absorption and enhance the segregation and conveyance of photogenerated charge carriers, leading to remarkable photocatalytic outcomes. Furthermore, we consolidate perspectives on the nonmetal doping of TiO₂, which tailors the material to harness visible light more efficiently and bolsters the separation and transfer of photogenerated carriers. The incorporation of various anions is summarized for their potential to propel TiO₂’s photocatalytic capabilities. This review aspires to compile contemporary insights on ion-doped TiO₂, propelling the efficacy of photocatalytic hydrogen evolution and anticipating forthcoming advancements. Our work aims to furnish an informative scaffold for crafting advanced TiO₂-based photocatalysts tailored for water-splitting applications. Full article

The leaf area index (LAI) is one of the core parameters reflecting the growth status of vegetation. The continuous long-term observation of the LAI is key when assessing the dynamic changes in the energy exchange of ecosystems and the vegetation’s response indicators to climate change. The errors brought about by non-standard operations in manual LAI measurements hinder the further research utilization of this parameter. The long-term automatic LAI observation network is helpful in reducing errors from manual measurements. To further test the applicability of automatic LAI observation instruments in forest environments, this study carried out comparative validation research of the LAI-NOS (LAI automatic network observation system) at the Wanglang Mountain Ecological Remote Sensing Comprehensive Observation Station, China, comparing it with the results measured by the LAI-2200 Plant Canopy Analyzer (LI-COR, Lincoln, NE, USA), the LAI-probe handheld instrument, and a fisheye lens digital camera (DHP method). Instead of using the original “smoothest window” method, a new method, the “sunrise–sunset” method, is used to extract daily LAI-NOS LAI, and the corresponding confidence level is used to filter the data. The results of the data analysis indicate the following: LAI-NOS has a high data stability. The automatically acquired daily data between two consecutive days has a small deviation and significant correlations. Single-angle/multi-angle LAI measurement results of the LAI-NOS have good correlations with the LAI-2200 (R² = 0.512/R² = 0.652), the LAI-probe (R² = 0.692/R² = 0.619), and the DHP method (R² = 0.501/R² = 0.394). The daily LAI obtained from the improved method, when compared to the original method, both show the same vegetation growth trend. However, the improved method has a smaller dispersion. This study confirms the stability and accuracy of automatic observation instruments in mountainous forests, demonstrating the distinct advantages of automatic measurement instruments in the long-term ground observation of LAIs. Full article

The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm’s efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge. Full article

In the present work, predictive modelling and optimization with the adaptive network based fuzzy inference system (ANFIS) modelling of the mechanical properties of laser-coated NB/SiC/Ni welds was studied based on the Taguchi design by laser cladding. An ANFIS model based on a Sugeno type fuzzy inference system was developed for predicting the hardness properties of SiC/BN/Ni welds by laser cladding with experimental data required for network training and prediction. Based on analysis of variance, three important factors were taken as inputs for the fuzzy logic inferences, while the hardness properties were taken as the output of the ANFIS. The microstructure of welds was analysed using scanning electron microscopy with an energy-dispersive X-Ray spectrometer. Highly developed leaf-like dendrites and eutectic crystals were found in some areas of the melting zone for the BN/SiC/Ni weld, which was significantly hardened. The ANFIS model based on Taguchi’s design provides a better pattern of response because the predicted and experimental values were highly similar. As a result, a satisfactory result was achieved between the predicted and experimental values of hardness in laser-coated NB/SiC/Ni welds, whereby the success and validity of the method was verified. Full article

Edible fungi are well known for their rich nutrition and unique flavor. However, their post-harvest shelf-life is relatively short, and effective post-harvest preservation techniques are crucial for maintaining their quality. In recent years, many new technologies have been used for the preservation of edible fungi. These technologies include cold plasma treatment, electrostatic field treatment, active packaging, edible coatings, antimicrobial photodynamic therapy, and genetic editing, among others. This paper reviews the new methods for post-harvest preservation of mainstream edible fungi. By comprehensively evaluating the relative advantages and limitations of these new technologies, their potential and challenges in practical applications are inferred. The paper also proposes directions and suggestions for the future development of edible fungi preservation, aiming to provide reference and guidance for improving the quality of edible fungi products and extending their shelf-life. Full article

Nitrogen (N) is the most important nutrient in coffee, with a direct impact on productivity, quality, and sustainability. N uptake by the roots is dominated by ammonium (NH₄⁺) and nitrates (NO₃⁻), along with some organic forms at a lower proportion. From the perspective of mineral fertilizer, the most common N sources are urea, ammonium (AM), ammonium nitrates (AN), and nitrates; an appropriate understanding of the right balance between N forms in coffee nutrition would contribute to more sustainable coffee production through the better N management of this important crop. The aim of this research was to evaluate the influences of different NH₄-N/NO₃-N ratios in coffee from a physiological and agronomical perspective, and their interaction with soil water levels. Over a period of 5 years, three trials were conducted under controlled conditions in a greenhouse with different growing media (quartz sand) and organic soil, with and without water stress, while one trial was conducted under field conditions. N forms and water levels directly influence physiological responses in coffee, including photosynthesis (Ps), chlorophyll content, dry biomass accumulation (DW), nutrient uptake, and productivity. In all of the trials, the plants group in soils with N ratios of 50% NH₄-N/50% NO₃-N, and 25% NH₄-N/75% NO₃-N showed better responses to water stress, as well as a higher Ps, a higher chlorophyll content, a higher N and cation uptake, higher DW accumulation, and higher productivity. The soil pH was significantly influenced by the N forms: the higher the NO₃⁻-N share, the lower the acidification level. The results allow us to conclude that the combination of 50% NH₄-N/50% NO₃-N and 25% NH₄-N/75% NO₃-N N forms in coffee improves the resistance capacity of the coffee to water stress, improves productivity, reduces the soil acidification level, and improves ion balance and nutrient uptake. Full article

The influence of damping and friction performance of grinding wheels on precision grinding was explored for the first time. GO hybrid PU-modified PF copolymers were prepared by in situ synthesis and adopted as a matrix for fabricating grinding wheels. FT-IR, DSC, TG, and mechanical property tests showed the optimal modification when PU content was 10 wt% and GO addition was 0.1 wt%. Damping properties were investigated by DMA, and tribological characteristics were measured by sliding friction and wear experiments. The worn surfaces and fracture morphologies of GO hybrid PU–PF copolymers were observed by SEM. Distribution of components on the worn surfaces was explored by Raman mapping and EDS. The research results revealed that the PU component tended to be dispersed around the edges of corundum abrasives acting as a buffer layer of abrasive particles, which could provide micro-damping characteristics for abrasives, making the grinding force more stable during precision machining and facilitating a smoother surface quality of the workpiece. Full article

Clostridioides difficile infection (CDI) is the leading cause of nosocomial antibiotic-associated diarrhea, and colitis, with increasing incidence and healthcare costs. Its pathogenesis is primarily driven by toxins produced by the bacterium C. difficile, Toxin A (TcdA) and Toxin B (TcdB). Certain strains produce an additional toxin, the C. difficile transferase (CDT), which further enhances the virulence and pathogenicity of C. difficile. These toxins disrupt colonic epithelial barrier integrity, and induce inflammation and cellular damage, leading to CDI symptoms. Significant progress has been made in the past decade in elucidating the molecular mechanisms of TcdA, TcdB, and CDT, which provide insights into the management of CDI and the future development of novel treatment strategies based on anti-toxin therapies. While antibiotics are common treatments, high recurrence rates necessitate alternative therapies. Bezlotoxumab, targeting TcdB, is the only available anti-toxin, yet limitations persist, prompting ongoing research. This review highlights the current knowledge of the structure and mechanism of action of C. difficile toxins and their role in disease. By comprehensively describing the toxin-mediated mechanisms, this review provides insights for the future development of novel treatment strategies and the management of CDI. Full article

The DC distribution network is a low-inertia system, which is very sensitive to load disturbance, system failure, and other factors. By adding virtual capacitance to the converter connected to the power supply, the virtual inertia of the DC power grid can be improved. Firstly, this paper proposes a strategy for adjusting virtual capacitance based on the voltage change rate to achieve adaptive control of virtual inertia, which enables the converter to quickly absorb or release energy during power fluctuations. Secondly, the adaptivity of the strategy is improved and the main control parameters in the proposed control method are qualitatively analyzed. Finally, the four-terminal photovoltaic storage DC distribution network system is constructed. Through Simulink, the adaptive virtual inertia control is incorporated on the battery side to simulate and validate the effectiveness of the strategy and the rationality of parameter analysis. The results show that this method can provide flexible and adjustable inertia support for DC grids and improve the voltage stability of DC grids. Full article

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI’s GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance. Full article

Background: In an era of dramatic technological progress, the consequent economic transformations, and an increasing need for an adaptable workforce, the importance of education has risen to the forefront of the social discourse. The concurrent increase in the awareness of issues pertaining to social justice and the debate over what this justice entails and how it ought to be effected, feed into the education policy more than ever before. From the nexus of the aforementioned considerations, concern about the so-called education gap has emerged, with worldwide efforts to close it. Methods: I analyze the premises behind such efforts and demonstrate that they are founded upon fundamentally flawed ideas. Results: I show that in a society in which education is delivered equitably, education gaps emerge naturally as a consequence of differentiation due to talents, the tendency for matched mate selection, and the heritability of intellectual traits. Conclusion: I issue a call for a redirection of efforts away from the ill-founded idea of closing the education gap to the understanding of the magnitude of its unfair contributions, as well as to those social aspects that can modulate it in accordance with what a society deems fair according to its values. Full article

Solar photovoltaic (PV) systems are becoming increasingly popular because they offer a sustainable and cost-effective solution for generating electricity. PV panels are the most critical components of PV systems as they convert solar energy into electric energy. Therefore, analyzing their reliability, risk, safety, and degradation is crucial to ensuring continuous electricity generation based on its intended capacity. This paper develops a failure mode and effects analysis (FMEA) methodology to assess the reliability of and risk associated with polycrystalline PV panels. Generalized severity, occurrence, and detection rating criteria are developed that can be used to analyze various solar PV systems as they are or with few modifications. The analysis is based on various data sources, including field failures, literature reviews, testing, and expert evaluations. Generalized severity, occurrence, and detection rating tables are developed and applied to solar panels to estimate the risk priority number (RPN) and the overall risk value. The results show that the encapsulant, junction box, and failures due to external events are the most critical components from both the RPN and risk perspectives. Delamination and soiling are the panels’ most critical FMs, with RPN values of 224 and 140, respectively, contributing 16.2% to the total RPN. Further, moderately critical FMs are also identified which contribute 56.3% to the RPN. The encapsulant is the most critical component, with RPN and risk values of 940 (40.30%) and 145 (23.40%), respectively. This work crucially contributes to sustainable energy practices by enhancing the reliability of solar PV systems, thus reducing potential operational inefficiencies. Additionally, recommendations are provided to enhance system reliability and minimize the likelihood and severity of consequences. Full article