This study focuses on the successful production and detailed characterization of surfactant-aided bismuth vanadate (BiVO4) nanoparticles (NPs), designed specifically to enhance their use in environmental remediation. The BiVO4 NPs were synthesized using a simple co-precipitation method, followed by the addition of a surfactant before the final calcination step. The researchers proposed that this surfactant-assisted approach would allow for precise control over the particle size, morphology, and surface area, which, in turn, would significantly boost the material's catalytic action. The resulting BiVO4 NPs were thoroughly analyzed using various techniques, including X-ray diffraction (XRD), Fourier transform infra-red microscopy (FTIR), Energy dispersive X-ray microscopy (EDX), Raman spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and UV-Vis diffuse reflectance spectroscopy (DRS). These tests confirmed the formation of crystalline BiVO4 NPs with highly desirable structural and optical properties, particularly strong visible-light absorption. The prepared BiVO4 NPs demonstrated exceptional efficiency in the photocatalytic degradation of organic contaminants (such as selected dyes or pharmaceuticals) when exposed to visible light. The rate of degradation was markedly superior to that achieved by BiVO4 synthesized without the surfactant. This enhanced performance is attributed to the resulting better charge separation and an increased number of available active sites on the nanoparticle surface. Furthermore, the surfactant-functionalized BiVO4 NPs also exhibited excellent antibacterial activity against both Gram-negative and Gram-positive bacterial strains, thereby establishing the material as a truly multi-functional agent. The combined, improved performance in both photocatalysis and antibacterial activity positions these surfactant-assisted BiVO4 NPs as a promising, cost-effective, and highly active nanomaterial for advanced applications in wastewater treatment and the preservation of public health.

The industrial sector, a significant contributor to global energy demand, is experiencing a vital transition towards sustainable practices while maintaining production efficiency. The implementation of peak shaving electricity, a strategy that reduces consumption during periods of peak demand, presents a viable solution. This approach reduces greenhouse gas emissions and energy costs, benefiting both the environment and the economy. Recent advancements in the integration of solar photovoltaics (PV), battery energy storage system (BESS), and demand response programs have enhanced the appeal of peak shaving using with vendor controller and reliable communication system. This integrated approach has attracted considerable attention for its potential to optimize energy use while maintaining industrial operations, providing a pathway to responsible industrial sustainability. This paper presents application of optimal BESS management with integrating solar PV for industrial peak shaving using real-time demand response data and standard Modbus TCP/IP communication systems. This article identifies key themes, including objectives, technologies employed, and techniques for implementation. A case study of a California Waste Management facility describes the implementation of hybrid solar photovoltaic systems, a battery energy storage system, and electric vehicle (EV) charging infrastructure. These systems are capable of directly powering operations, storing solar energy in batteries, feeding excess energy into the grid, and transitioning to grid-supplied power as required. This case study demonstrated a notable 13.87% reduction in energy costs and a 22.9% decrease in CO2 emissions. This study presents the Industrial Peak Shaving framework, designed to promote sustainable industrial practices and guide future research and implementation.

In Awka and most parts of Anambra State, plastering is commonly carried out using the cheapest and nearest available materials like river-bed sand dredged from the Onitsha reach of the River Niger or clayey borrow-pit soils excavated locally, which are often mixed by eye, leading to frequent cracking, blistering, delamination, poor bonding, and patchy finishes. This study therefore characterized the geotechnical, physico-chemical, and mineralogical properties of borrow-pit soils from Amansea and Ebenebe, river-bed sand from Onitsha, and four laboratory-prepared blends at 80/20 and 60/40 (sand/soil) ratios using particle-size analysis, Atterberg limits, specific gravity, Standard Proctor compaction, X-ray fluorescence (XRF), and X-ray diffraction (XRD). Results showed that all materials are highly siliceous (SiO₂ 77–87 wt.%) and quartz-dominated (86–96 wt.%) with very low fines content (< 0.6 %), making them essentially non-plastic despite the clayey appearance of the borrow-pit soils (kaolinite only 3–7 wt.%). Blending Onitsha river-bed sand with borrow-pit soils significantly reduced fines, water demand, and plasticity while increasing maximum dry density and specific gravity. The 60 % Onitsha + 40 % Amansea blend exhibited the optimum combination: highest maximum dry density (1.86 Mg/m³), low optimum moisture content (11 %), very low fines (0.39 %), and the cleanest oxide profile, clearly outperforming the individual raw materials. The widespread plaster defects observed locally are thus attributable to the use of unblended or poorly proportioned materials, while a simple, controlled 60:40 blend offers a strong, shrinkage-resistant, and sustainable plastering aggregate using only locally available resources.

The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and network data analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. During the last few years computer applications have undergone a dramatic transformation from simple data processing to machine learning, thanks to the availability and accessibility of huge volumes of data collected through sensors and the internet. The idea of machine learning demonstrates and propagates the fact that the computer has the ability to improve itself with the passage of time. The western countries have shown great interest on the topic of machine learning, computer vision, and pattern recognition via organizing conferences, workshops, collective discussion, experimentation, and real-life implementation. This study on machine learning and computer vision explores and analytically evaluates the machine learning applications in computer vision and predicts future prospects. The study has found that the machine learning strategies in computer vision are supervised, un-supervised, and semi- supervised. The commonly used algorithms are neural networks, k-means clustering, and support vector machines. The most recent applications of machine learning in computer vision are object detection, object classification, and extraction of relevant information from images, graphic documents, and videos. Additionally, Tensor flow, Faster- RCNN-Inception-V2 model, and Anaconda software development environment are used to identify cars and persons in images.

Organizational crisis communication on social media has become critical for reputation management, yet systematic empirical evidence remains limited. This study employs Natural Language Processing and machine learning to analyze 17,500 tweets from 50 major organizational crises across 14 industries. Using multi-model sentiment analysis (VADER, TextBlob), emotion detection (NRC Lexicon), and 14 machine learning algorithms, we investigate communication strategies, sentiment patterns, and predictive modeling of message effectiveness. Results reveal organizations predominantly employ information-focused strategies (61.7%), with a moderate sentiment gap between firm communications (TextBlob polarity: 0.164) and public responses (-0.002). Sentiment shows negligible correlation with total engagement (r = -0.000), though negative sentiment generates significantly higher engagement than positive sentiment (t = -2.148, p = 0.032). Machine learning achieves modest predictive accuracy (53.07%, Naive Bayes), demonstrating both potential and limitations of AI-assisted crisis management. This research contributes computational evidence to crisis communication theory, establishes methodological innovations for large-scale text analysis in IS research, and provides realistic assessments of data-driven crisis management capabilities.

In Pakistan, land subsidence has become a serious geohazard, with the major factors being rapid urbanization, overextraction of groundwater, overloading of infrastructural facilities, and natural compaction. Conventional terrestrial methods of monitoring, such as leveling and GNSS, are highly accurate but spatially sparse, costly, and cannot monitor deformation over a regional scale. Here, satellite-based Synthetic Aperture Radar Interferometry (InSAR) especially Persistent Scatterer InSAR, provides a stable and economical method of long-standing subsidence monitoring on extensive and uneven surfaces. The paper demonstrates an attempt to integrate PSInSAR with multi-temporal SAR data to map and analyze subsidence trends of the area land of selected urban and peri-urban areas of Pakistan. The PSInSAR method allows the stable radar target of interest to be identified and allows the time series of millimeter-scale surface deformation to be extracted, which is likely to reduce the atmospheric disturbance and time-correlation effects of traditional InSAR methods. This study will measure spatial variability, temporal change, and subsidence hotspots of anthropogenic and geological nature by using the high-resolution SAR datasets. The paper also compares the patterns of deformation that are observed with the root causes that include: groundwater depletion, land-use change, and expansion of infrastructure. The results present important information about the processes of subsidence in data-sparse areas of Pakistan and indicate that PSInSAR is a valid instrument of monitoring hazard control, urban development and sustainable resources utilization. This study helps to enhance geospatial decision-support systems and provides a scientific foundation of risk-based policymaking in the regions with subsidence risks.

Hybrid composite structures (e.g., carbon–glass laminates, fiber–metal laminates, and multi-material sandwich panels) offer superior stiffness-to-weight performance but exhibit complex, multi-mode damage mechanisms such as matrix cracking, fiber breakage, delamination, and interface debonding. These damage modes are often difficult to detect early and expensive to simulate at full structural scale with high fidelity. This paper proposes an integrated framework that combines multi-scale progressive damage simulation with machine learning (ML)–assisted damage inference and load optimization. At the microscale and mesoscale, damage initiation and evolution are captured using established composite failure criteria and degradation laws (e.g., Hashin-type mechanisms), while structural-scale response is computed using reduced-order surrogates calibrated from multi-scale results. On the data side, guided-wave/shock-response features and simulated strain-field descriptors are mapped to damage states using supervised and uncertainty-aware ML models. Finally, a load optimization module minimizes peak interlaminar stresses and damage growth rate under service constraints. A case study on a hybrid laminate panel demonstrates that the proposed pipeline can (i) identify early delamination and matrix cracking signatures with high classification performance, and (ii) reduce damage-driving stress metrics through ML-guided load redistribution.

Traditional methods of wastewater treatment, in the past, have aimed at removing pollutants and compliance to regulations as a result of which have proven to be inefficiency and resource wastage. Nevertheless, growing demands of water scarcity, climate change, and requirements to adopt a circular economy have fuelled the shift into next-generation biological processes, which redefine the concept of wastewater as a resource, as opposed to a waste stream. This review assesses critically emerging paradigms in biological treatment that go beyond traditional activated sludge systems and incorporate the new developments in the fields of microbial ecology, synthetic biology and bioelectrochemical systems and nature-inspired engineering. Special focus is made on new microbial consortia, designed metabolic routes, and system-wide process advancement that allow the recovery of nutrients, generation of bioenergy, and the manufacture of value-added biochemicals in a better way. The article also assesses the role of hybrid biological systems in the treatment of wastewater under energy-neutral or energy-positive processes, including microbial electrochemical systems and algae-bacteria systems. The problem of techno-economic feasibility, operational resilience, and scalability are discussed systematically to reduce the gap between the innovation over the laboratory scale and its application in the real world. This article identifies the key gaps in knowledge, regulatory issues, and barriers to integration that may not be able to be easily adopted since they point to the recent advances in various fields. Finally, the review also provides a future-based structure of planning sustainable wastewater treatment processes in accordance with a circular bioeconomy, with future-generational biological processes being the core elements of the future water infrastructure in cities and industry.

Carbon Capture, Utilization and storage (CCUS) continue to emerge as the most viable technology to reduce the emission of greenhouse gases around the world, the bulk of which is in the hard to abate industries. This paper has presented a systematic review of the existing technological implementation, the key challenges that have been identified, the gaps in knowledge, and also the emerging innovations that have been continuing to shape the field. The review incorporates information in the world deployment databases, state reports, and peer reviewed libraries. CCUS technologies have reached maturity in the realms of capture and storage but the large scale deployment of capturing technology has been limited because of the high cost, the presence of adequate infrastructure and due to policy uncertainty. The review paper presents some recommendations on how to enhance efficiency, lower costs and achieve sustainable industrial integration with net zero emissions.