Search Results (1,658)

Pseudomonas syringae pv. syringae and Xanthomonas arboricola pv. juglandis are the causal agents of bacterial canker in cherry and walnut blight, respectively, which cause significant production losses worldwide. These diseases have traditionally been controlled by copper-based agrochemicals and, more recently, antibiotics. However, the prolonged use of these compounds has led to the emergence of resistant bacterial strains. The search for new, efficient, and environmentally friendly biocontrol alternatives has intensified. Phages are promising candidates due to their ability to specifically infect and lyse bacterial pathogens. Endolysin enzymes are responsible for bacterial cell wall degradation, and although they have been extensively studied in medical and veterinary contexts, their application in agriculture remains limited. In this study, 17 putative endolysins were identified from bacteriophages infecting X. arboricola pv. juglandis and P. syringae pv. syringae. Based on conserved domain analyses, 12 were classified as glycosidases, four as amidases, and one as an endopeptidase. From these, a recombinant amidase (LysP) and a recombinant glycosidase (LysX) were expressed in E. coli, purified, and evaluated as pure enzymes. Both endolysins exhibited significant antimicrobial activity, reducing P. syringae pv. syringae viability by 62–78.3% and X. arboricola pv. juglandis viability by 51.5–53.1%, respectively. These findings highlight these recombinant endolysins as promising candidates for the development of biocontrol strategies against bacterial plant pathogens. Full article

Background: Cassava (Manihot esculenta Crantz) ranks as the sixth largest food crop worldwide and serves as an important cash and energy crop. Heavy-metal-associated isoprenylated plant proteins (HIPPs) are metallochaperones involved in metal homeostasis and stress adaptation in vascular plants. However, research on the identification and function of HIPPs in cassava has been poorly explored. Methods: This study conducted a pan-genome-wide investigation to identify and characterize MeHIPPs in 31 cassava accessions. Subsequent analyses examined their physicochemical properties, subcellular localization, phylogeny, Ka/Ks, chromosomal localization, synteny, gene structure, and cis-acting elements. Additionally, the expression profiles of MeHIPPs in different tissues and cell subsets and under different stress conditions were analyzed using transcriptome data and quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 59 MeHIPP pan-genes were identified, including five core genes, 22 softcore genes, 17 dispensable genes, and 15 private genes, which were unevenly distributed on chromosomes. Based on phylogenetic analysis, these genes were classified into five major subgroups. Evolutionary analyses indicated that segmental duplication predominated in family expansion and that most members may be subjected to purifying selection. Cis-element analysis highlighted the importance of MeHIPPs in plant adaptation to environmental stress. The expression profiles suggested widespread involvement of MeHIPP genes in response to Xanthomonas phaseoli pv. manihotis (Xpm) infection and drought stress. Different MeHIPP genes exhibited varying transcript levels in different tissues and cell subsets. qRT-PCR analysis revealed that the selected MeHIPP genes had distinct expression patterns under Cd stress. Conclusions: This study provides valuable insights into the functional characteristics of MeHIPP genes and their evolutionary relationships, laying a theoretical foundation for further functional research on stress resistance. Full article

The article presents the current state of knowledge on genetic modifiers of ovarian cancer risk in women carrying pathogenic variants (PVs) in the BRCA1 and BRCA2 genes, which are major contributors to hereditary susceptibility to this malignancy. Although PV carriers have high disease penetrance (BRCA1: ~40% and BRCA2: 11–27%), substantial variability in individual risk is observed, suggesting the influence of additional genetic variants. Background: Ovarian cancer is characterized by late detection and high mortality, and a significant portion of risk among BRCA1/2 carriers is shaped by reproductive and environmental factors as well as genetic modifiers. The article emphasizes that carriers of the same BRCA PV can exhibit markedly different risk levels depending on additional variants that modulate key biological processes, such as DNA repair, cell cycle regulation, and apoptosis. Methods: A systematic literature search covering the years 1996–2025 was conducted in the PubMed database. Initially, 734 publications were identified; after removing duplicates, thematically irrelevant articles, non-full-text papers, and studies not meeting the inclusion criteria, 47 articles were included in the review. These studies covered candidate gene analyses, GWAS, and data from the CIMBA consortium, which enables the examination of large cohorts of PV carriers. Results: The review identified numerous variants associated with increased or decreased ovarian cancer risk in BRCA1 carriers, including the following: OGG1, DR4, MDM2, CYP2A7, CASP8, ITGB3, HRAS1, TRIM61, and MTHFR. The reviewed studies also identified both protective and risk-increasing variants among BRCA2 PV carriers: UNG, TDG, and PARP2, and haplotypes in ATM, BRIP1, BARD1, MRE11, RAD51, and 9p22.2. The analysis identified 11 variants affecting both BRCA1 and BRCA2 carriers, most of which increase risk, including the following: IRS1, RSPO1, SYNPO2, BABAM1, MRPL34, PLEKHM1, and TIPARP. Protective variants include BNC2 and LINC00824. The only SNP reaching genome-wide significance (p < 5 × 10⁻⁸) was in BNC2. Conclusions: The article summarizes the growing number of genetic modifiers of ovarian cancer risk among BRCA1/2 carriers and highlights their potential to improve individualized risk assessment, enhance patient stratification, support personalized prevention and surveillance strategies, deepen the understanding of disease biology, and identify potential therapeutic targets. Full article

This study focuses on estimating the nine parameters of the three-diode model (3DM) for photovoltaic (PV) cells by integrating the Atan-Sinc Optimization Algorithm (ASOA) with the Newton–Raphson (NR) method. The ASOA, a population-based metaheuristic approach inspired by the behaviors of the Sech and Tanh functions, systematically generates candidate solutions for the complete set of parameters in the 3DM. For each of these solutions, the NR method is employed to solve the transcendental equation governing the solar cell model, facilitating a precise evaluation of the associated objective function. To guide the parameter estimation process, experimental current-voltage (I-V) and voltage-power (V-P) curves are utilized. The robustness of the proposed methodology is validated through studies on both monocrystalline and polycrystalline solar cells. Computational results reveal that the ASOA effectively navigates the parameter space, while the NR method provides accurate evaluations, resulting in reliable and precise parameter estimations. All numerical validations were conducted using MATLAB software, version 2024b. Full article

The growing demand for high-efficiency photovoltaic (PV) technologies has intensified interest in advanced cell architectures, including hybrid passivated back contact (HPBC) solar cells. Conventional solder-based interconnection processes require high thermal budgets, which can induce thermomechanical stress and lead to performance degradation in thin back-contact cell structures. In this study, electrically conductive adhesive (ECA) interconnection is investigated as a low-thermal-budget, solder-free alternative for HPBC solar cells. The curing behavior of an acrylic-based, silver-filled ECA is systematically examined by controlling the upper lamp temperature and the welding time during the interconnection process. Electrical performance is evaluated through current–voltage characterization, fill factor, and series resistance analysis, while interfacial microstructural evolution is examined using scanning electron microscopy. The results identify a well-defined processing window in which adequate curing enables stable electrical contact formation. In contrast, both insufficient curing and excessive curing result in degraded electrical performance. To assess practical applicability, HPBC modules with an industry-relevant size of ~1000 × 1160 mm² are fabricated and evaluated using electroluminescence imaging and I–V measurements. By identifying a robust curing window and demonstrating its successful transfer from string-level interconnections to full-size HPBC modules, this study establishes a practical, low-thermal-budget, solder-free interconnection strategy for advanced back-contact PV architectures. Full article

This work presents an experimental assessment of radiative cooling (RC) films and compound parabolic concentrator (CPC) optics integrated into systems relevant for smart cities: LED street luminaires and small photovoltaic (PV) and thermoelectric (TE) modules used as energy-harvesting (EH) sources for IoT devices. Using commercial RC film and simple 2D/3D CPC geometries, we conducted outdoor measurements under realistic conditions. For a commercial LED luminaire, several configurations were compared (painted aluminum reference, full RC coverage of the head, partial RC strips above the LED and driver, and RC combined with CPCs), recording surface temperatures during daytime and nighttime operation. In parallel, single-junction PV cells and Peltier-type TE generators were mounted on aluminum plates in three configurations: reference, RC-coated, RC + 3D-CPC. Their surface temperatures and open-circuit (OC) voltages were monitored in daylight. Across all campaigns, RC consistently reduced device or surface temperatures by a few degrees Celsius compared to the reference, with larger reductions under higher irradiance. For PV and TE modules, thermal differences produced small but measurable increases in OC voltage—percent-level for PV, millivolt-level for TE. CPCs generally preserved or slightly enhanced the cooling effect in some configurations, acting as incremental modifiers rather than primary drivers. The experiments are deliberately exploratory and provide initial experimental evidence that RC integration can be beneficial in real devices. They establish an empirical baseline for future work on long-term, multi-season campaigns, electrical characterization, optimized materials/optics, and system-level prototypes in smart-city lighting and IoT EH applications. Full article

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

The study establishes a comprehensive mathematical modeling framework for solar-driven hydrogen production by integrating a triple-diode photovoltaic (PV) model, an alkaline electrolyzer, and a hydrogen turbine (H₂T), subsequently using hybrid power utilization to optimize hydrogen output. The Triple-Diode Model (TDM) accurately reproduces the electrical performance of a 144-cell photovoltaic module under standard test conditions (STC), enabling precise calculations of hourly maximum power point outputs based on real-world conditions of global horizontal irradiance and ambient temperature. The photovoltaic system produced 1.07 MWh during the summer months (May to September 2025), which was sent straight to the alkaline electrolyzer. The electrolyzer, using Specific Energy Consumption (SEC)-based formulations and Faraday’s law, produced 22.6 kg of green hydrogen and used around 203 L of water. The generated hydrogen was later utilized to power a hydrogen turbine (H₂T), producing 414.6 kWh, which was then integrated with photovoltaic power to create a hybrid renewable energy source. This hybrid design increased hydrogen production to 31.4 kg, indicating a substantial improvement in renewable hydrogen output. All photovoltaic, electrolyzer, and turbine models were integrated into a cohesive MATLAB R2024b framework, allowing for an exhaustive depiction of system dynamics. The findings validate that the amalgamation of H₂T with photovoltaic-driven electrolysis may significantly improve both renewable energy and hydrogen production. This research aligns with Saudi Vision 2030 and global clean-energy initiatives, including the Paris Agreement, to tackle climate change and its negative impacts. An integrated green hydrogen system, informed by this study’s findings, could significantly improve energy sustainability, strengthen production reliability, and augment hydrogen output, fully aligning with economical, technical, and environmental objectives. Full article

The rapid expansion of China’s photovoltaic (PV) industry has led to a significant increase in decommissioned PV modules. To address the high energy consumption and environmental impact of traditional recycling techniques, this study proposes a novel method that integrates DMPU solvent recycling with pyrolysis for recovering PV cell sheets. DMPU, an organic solvent with low volatility, non-toxicity, and excellent recyclability, was used in this study. The effects of temperature and treatment duration on the structural integrity of silicon cell sheets were systematically evaluated, establishing optimal parameters: immersion in DMPU at 200 °C for 60 min, followed by pyrolysis at 480 °C for 60 min. A case study was conducted on a small-scale recycling facility with a daily processing capacity of 200 standard PV panels, encompassing system boundaries such as transportation, pretreatment, and pyrolysis. The recycling process consumed 2.14 × 10⁹ kJ of energy annually, reducing CO₂ emissions by 9357.2 tons. Compared to conventional methods such as pyrolysis, mechanical dismantling, and chemical dissolution, the proposed approach employing a green, recyclable solvent markedly reduces energy consumption and carbon emissions, offering notable environmental benefits. Full article

The pepper Bs2 resistance gene confers resistance to susceptible Solanaceae plants against pathogenic strains of Xanthomonas campestris pv. vesicatoria carrying the avrBs2 avirulence gene. Previously, we generated Bs2-transgenic Citrus sinensis plants that exhibited enhanced resistance to citrus canker caused by Xanthomonas citri subsp. citri (Xcc), although the underlying mechanisms remained unknown. To elucidate the molecular basis of the early defense response, we performed a comparative transcriptomic analysis of Bs2-expressing and non-transgenic plants 48 h after Xcc inoculation. A total of 2022 differentially expressed genes (DEGs) were identified, including 1356 up-regulated and 666 down-regulated genes. In Bs2-plants, 36.8% of the up-regulated DEGs were associated with defense responses and biotic stress. Functional annotation revealed major changes in genes encoding receptor-like kinases, transcription factors, hormone biosynthesis enzymes, pathogenesis-related proteins, secondary metabolism, and cell wall modification. Among hormone-related pathways, genes linked to ethylene biosynthesis and signaling were the most strongly regulated. Consistently, endogenous ethylene levels increased in Bs2-plants following Xcc infection, and treatment with an ethylene-releasing compound enhanced resistance in non-transgenic plants. Overall, our results indicate the Bs2 expression activates a complex defense network in citrus and may represent a valuable strategy for controlling canker and other Xanthomonas-induced diseases. Full article

Among the most promising alloys for photovoltaic applications is copper–indium–gallium–selenide (CIGS) because of its enhanced optical properties. This study examines the influence of current density and pulse timing on the electrodeposition of Cu(In, Ga)Se₂ (CIGS) thin films from a dilute electrolyte. It assesses how these parameters affect deposition quality, film characteristics, and device performance. CIGS absorber layers were electrodeposited using a pulsed-current method, with systematic variations in current density and pulse on/off durations in a low-concentration solution. The deposited precursors were subsequently selenized and incorporated into fully assembled CIGS solar cell architectures. Structural characteristics were analyzed by X-ray diffraction (XRD), whereas composition and elemental distribution were assessed by energy-dispersive X-ray spectroscopy (EDS). Optical properties pertinent to photovoltaic performance were evaluated through transmittance and reflectance measurements. The results indicate that moderate current densities, when combined with brief off-times, produce dense, microcrack-free films exhibiting enhanced crystallinity and near-stoichiometric Cu/(In + Ga) and Ga/(In + Ga) ratios, in contrast to films deposited at higher current densities and extended off-times. These optimized pulse parameters also produce absorber layers with advantageous optical band gaps and improved device performance. Overall, the study demonstrates that regulating pulse parameters in attenuated electrolytes is an effective strategy to optimize CIGS film quality and to facilitate the advancement of economical, solution-based fabrication methods for high-performance CIGS solar cells. Full article

Bypass diode failure, particularly in the short-circuit mode, remains an under-addressed reliability issue in photovoltaic (PV) modules, causing severe power suppression and often leading to premature disposal of otherwise functional units. This study presents a non-destructive, field-applicable plug-in repair protocol for restoring modules affected by short-circuited bypass diodes. From twenty-two field-deployed modules, nine were analyzed in detail under healthy, single-fault, and dual-fault conditions. Controlled diode faults were introduced and subsequently repaired using commercially available plug-in bypass diodes. Electroluminescence (EL) imaging, current–voltage (I–V) testing, and extraction of series and shunt resistances were performed before and after repair. Results show that a single shorted diode deactivates one substring, reducing power by ~34–37%, while dual faults suppress over two-thirds of the active area, causing power losses above 67%. After repair, power deviation decreased to <3% for single faults and <7% for dual faults, with shunt resistance increasing by 52–262%, confirming removal of diode-induced leakage paths. Series resistance remained largely unchanged except in modules with irreversible cell-level damage accumulated during prolonged faulty operation. The findings demonstrate that short-circuited bypass diode faults are readily repairable and that component-level intervention can restore module performance, extend operational lifetime, and reduce unnecessary PV recycling. Full article

Decarbonizing industry, improving urban sustainability, and expanding clean energy use are key global priorities. This study presents a techno-economic analysis (TEA) and life-cycle assessment (LCA) of green hydrogen (GH₂) production via water electrolysis for low-carbon applications in the Central Asian region, with Uzbekistan considered as a representative case study. Solar PV and wind power are used as renewable electricity sources for a 44 MW electrolyzer. The assessment also incorporates recent advances in alkaline water electrolyzers (AWE) enhanced with metal–organic framework (MOF) materials, reflecting improvements in efficiency and hydrogen output. The LCA, performed using SimaPro, evaluates the global warming potential (GWP) across the full hydrogen production chain. Results show that the MOF-enhanced AWE system achieves a lower levelized cost of hydrogen (LCOH) at 5.18 $/kg H₂, compared with 5.90 $/kg H₂ for conventional AWE, with electricity procurement remaining the dominant cost driver. Environmentally, green hydrogen pathways reduce GWP by 80–83% relative to steam methane reforming (SMR), with AWE–MOF delivering the lowest footprint at 1.97 kg CO₂/kg H₂. In transport applications, fuel cell vehicles powered by hydrogen derived from AWE–MOF emit 89% less CO₂ per 100 km than diesel vehicles and 83% less than using SMR-based hydrogen, demonstrating the substantial climate benefits of advanced electrolysis. Overall, the findings confirm that MOF-integrated AWE offers a strong balance of economic viability and environmental performance. The study highlights green hydrogen’s strategic role in the Central Asian region, represented by Uzbekistan’s energy transition, and provides evidence-based insights for guiding low-carbon hydrogen deployment. Full article

Root-knot nematodes (RKNs) of the Meloidogyne genus impact various plants, including crops, fruits, and vegetables. Few chemical control options exist globally, and many nematicides are banned due to health and environmental risks. This study tested a new nematicidal agent, the symbiotic bacterium Xenorhabdus indica, which was molecularly identified (PV845100). Cell-free culture supernatants of Xenorhabdus spp. and their biogenic Ag-NPs were used in nematicidal assays. Meloidogyne incognita showed high mortality rates of 95.3%, 74.6%, and 72.6% after 72 h of treatment with the X. indica filtrate at three concentrations. At the same concentrations, biogenic Ag-NPs resulted in 82.0%, 90.0%, and 85.3% mortality rates, respectively. After 72 h, hatchability decreased by 53%, 74.6%, and 72.6% for the X. indica filtrate and 82.0%, 90.0%, and 85.3% for Ag-NPs. Quantitative real-time PCR (Q-PCR) revealed that Mi-Ache1 expression was lower in M. incognita second-stage juveniles (J2s) treated with the filtrate and Ag-NPs after 72 h compared to controls. Mi-Ache2 expression was also decreased, but only slightly. Furthermore, both the X. indica filtrate and biogenic Ag-NPs were safe in human lung (WI-38) and skin (HFB4) cell lines. These findings suggest that bacterial filtrates and their biogenic Ag-NPs could serve as cost-effective, environmentally friendly alternatives to commercial nematicides. Full article

This paper evaluates the efficacy of the Black-Winged Kite Algorithm (BKA) for parameter estimation in single-, double-, and triple-diode photovoltaic (PV) models. This study targets key electrical parameters, including photocurrent, reverse saturation current, series, and shunt resistances, and diode ideality factor(s) using experimental I-V data from an RTC France silicon cell. Performance is assessed using the root mean square error (RMSE) and convergence behavior and benchmarked against established metaheuristics including the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and Ant Lion Optimizer (ALO). The results show that BKA achieves competitive RMSE values with stable convergence for the investigated dataset. BKA employs coupled exploration and exploitation updates inspired by hunting and migration behaviors, and its limited number of control parameters supports straightforward deployment in nonlinear PV identification tasks. The results support BKA as a viable optimization option for PV model fitting in this setting, while also reflecting the typical trade-offs between search diversity and computational effort inherent to population-based methods. Full article