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Ornamental fish keeping is the second most preferred hobby in the world and it provides a great opportunity for entrepreneurship development and income generation. Controlling the environment in ornamental fish farm is a considerable challenge because it is affected by a variety of parameters like water temperature, dissolved oxygen, pH, and disease occurrences. One particular interesting ornamental fish species is goldfish ( Carassius auratus ). Machine learning (ML) and deep learning technique have significant potential in analysing voluminous data collected from fish farm. Through this technique, the fish farmers can get insight on feeding behaviour, fish growth patterns, predict diseases/stress, and environmental factors affecting fish health. The aim of the study is to analyze the behavioural changes in goldfish due to alterations in environmental parameters (water temperature and dissolved oxygen). Decision tree, Naïve Bayes classifier, K-nearest neighbour (KNN), and linear discriminant analysis (LDA) were used to analyse the behavioural change data. To compare the performance between all four classifiers, cross validation and confusion matrix used. The cross-validation error of LDA, Naïve Bayes classification, KNN and decision tree was 19.86, 28.08, 30.14 and 13.78 respectively. Decision tree was proved to be the most accurate and effective classifier. Different temperature and DO range were taken to predict fish behaviour. Some findings are, the behaviour of fish was rest between temperature 37.85 °C and 40.535 °C, erratic when temperature was greater than or equal to 40.535 °C, gasping when temperature was between 37.85 and 40.535 °C and when DO concentration was less than 6.58 mg/L. Blood parameter analysis has been done to validate the change in external behaviours with change in physiological parameters.

Benzophenone-3 is an Emerging Pollutant having significant ecotoxicological effects on aquatic organisms, owing to its widespread use as a UV filter and stabilizer to prevent photodegradation of commercial products, and has a ubiquitous presence. The present study entails an investigation of the bioremediation potential of an integrated system of biochar and Lemna minor against Benzophenone-3 from aqueous solution through batch studies. This integration in spiked distilled and municipal wastewater yielded a total removal of 73.82% and 80.46% of Benzophenone-3, respectively. Quantitative analysis of the FTIR spectra showed Benzophenone-3 adsorption onto the sugarcane bagasse biochar in a similar trend, comparable with its experimental removal efficiency. The reactions followed pseudo-second-order and intraparticle diffusion kinetics and the Freundlich isotherm modelling. Metabolites of Benzophenone-3, namely 2,4-Dihydroxybenzophenone and the first report of 2,3,4-Trihydroxybenzophenone in plants, were observed in tissues of Lemna minor . Cation exchange and pore-filling in the case of biochar and plant uptake and metabolism in the case of Lemna minor were the major removal mechanisms. Physicochemical analysis of the municipal wastewater pre- and post-treatment revealed an improvement in its overall quality, rendering the water suitable for reuse. The study provides baseline data about the potential of biochar and Lemna minor in an integrated system for the remediation of Benzophenone-3. It finds potential application in constructed wetlands for the efficient, cost-effective and eco-friendly remediation of Emerging Contaminants upon further research.

Mono-cropping in the farming system decline in farm profit, climate change, and food insecurity are some of the major concerns that lead to unsustainability in the agricultural production system in the Eastern Gangetic Plains. A study was conducted for three years from June 2019 to June 2022 at Dr. Rajendra Prasad Central Agricultural University, Pusa, Bihar, India, to assess the profitable and best rice-based cropping system through crop diversification for sustainable agriculture. Ten different cropping sequences were exploited using randomised block design and replicated thrice, with the system productivity ranging from 8.70 to 24.95 t ha−1 under the different cropping sequences. The system productivity was increased by 187% and profitability by 299.52% in the maize − Cole crops − sesame cropping system over the rice − wheat cropping system. A diversified cropping system with black gram − maize + vegetable pea − sesbania possessed significantly more soil organic carbon (0.49%), bacterial population (47.85 × 106 cfu/g soil), azotobacter population (42.96 × 104 cfu/g soil), phosphate solubilising bacteria (20.72 × 106 cfu/g soil), dehydrogenase activity (4.39 µg TPF/g/h), fluorescein diacetate hydrolytic activity (17.28 µg fluorescein/g/h) and acid phosphatase activity (451.46 µg pNP/g/h), as well as urease activity (47.21 µg NH4+/g/h), relative to the rice–wheat cropping system. Therefore, the adoption of vegetables and legumes as diversified crops are viable options for enhancing productivity, profitability and soil health in the EGPs.

Fishing has progressed from a subsistence activity to a multi-billion-dollar industry, but overfishing has put significant pressure on fishery resources, causing management and financial challenges. Evaluation of the comparative economics and performance of different fishing systems to identify the best fishing system will help to promote sustainable fishing envisioned by UN SDG Agenda 2030. The study was conceptualized to analyze the performance of fishing systems integrating economic and multi-criteria analysis. Total of 150 fishers from five different fishing systems viz. multiday trawler, multiday gillnetter, purse and ring seiner, motorized and non-motorized (traditional) systems has been considered for the study with eleven performance indicators. Methodologies like fuzzy VIKOR (FVIKOR), a multi-criteria decision-making approach that ranks alternatives by measuring their distance from the ideal solution, cost–benefit analysis and descriptive statistics were adopted to analyze the data. The FVIKOR analysis revealed that the motorized crafts followed by multiday gillnetter, purse and ring seiner, multiday trawler and non-motorized craft exhibited the best performance respectively. And the benefit-cost (BC) analysis revealed the maximum profitability for multiday gill netters with B-C ratio of 1.52 followed by motorized (1.49), purse & ring seiners (1.45), multiday trawler (1.20) and non-motorized (1.07) fishing systems. In contrast to the traditional deterministic and mono-criterial approach, integrating economic analysis with fuzzy multicriteria framework has helped to improve the knowledge of various fishing systems. FVIKOR methodology allows analysis of comprehensive performance of fishing systems more efficiently and conveniently. It is very helpful to aggregate the different indicators into a single composite index, making it easier to grasp the results, conveying relevant information for decision making in fisheries management.

Shrimp aquaculture is a major contributor to the global seafood supply, with India playing a key role. However, disease outbreaks and suboptimal farm management continue to cause substantial production losses. This study presents an integrated IoT–machine learning framework for real-time monitoring, behavioural analysis, and early detection of acute stress in shrimp culture systems. Environmental data collected through IoT sensors were combined with computer vision and machine learning techniques to analyse shrimp behaviour under varying water quality conditions. A YOLOv5 deep learning model enabled reliable underwater shrimp detection and tracking, achieving 84% detection accuracy. Behavioural changes driven by environmental stressors were predicted using machine learning models, with Decision Tree and Naïve Bayes classifiers achieving accuracies of 92% for pH-related responses and 88% for dissolved oxygen-related responses, respectively. Predicted behavioural anomalies were further validated through differential hemocyte count analysis, establishing a physiological link between environmental stress and observed behaviour. The proposed framework demonstrates a practical, data-driven approach for early stress detection, supporting reduced mortality, improved farm management, and sustainable shrimp production aligned with the Sustainable Development Goals.

Aquaculture, particularly shrimp farming, contributes to GHG emissions due to protein-rich feed inputs and reduced enzymatic activity in saline sediments, which affects soil health. A 75-day field study evaluated the role of PSB in reducing GHG emissions and improving sediment enzyme activity. PSB was applied at two rates: 20 kg/200 m 2 (1 ton ha -1 , T1) and 40 kg/200 m 2 (2-ton ha -1 , T2). Biochar application significantly ( p < 0.05) increased SMBC levels, reaching 452.43 ± 1.71 mg kg -1 (9.68%) in T1 and 487.99 ± 2.13 mg kg -1 (13.73%) in T2, compared with the control (429.14 ± 1.32 mg kg -1 ). Bacterial plate counts increased by 32.22% in T1(59.50 ± 1.50 CFU g -1 ) and 52.22% in T2 (68.50 ± 3 CFU g -1 ), relative to the control (45.00 ± 2 CFU g -1 ). Sediment enzymatic activities improved significantly ( p < 0.05): FDA activity increased by 9.66% in T1 (25.96 ± 1.78 μg g -1 h -1 ) and 24.49% in T2 (33.13 ± 2.05 μg g -1 h -1 ), while DHA and ALP increased by 25.55% (40.10 ± 2.88 µg TPF g -1 24 h -1 ) and 63.18% (52.11 ± 2.20 µg TPF g -1 24 h -1 ), and 9.70% (63.89 ± 1.18 µg PNP g -1  h -1 ) and 24.48% (72.50 ± 2.36 µg PNP g -1  h -1 ) in T1 and T2, respectively but only T2 showed statistically significant increases in DHA and ALP ( p < 0.05). Biochar application significantly influenced sediment characteristics ( p < 0.05). By day 75, sediment organic carbon in T2 was 77.9% (1.21%) higher than the control (0.68%). Similarly, available phosphorus increased by 2.22% in T1 (7.36 mg kg -1 ) and 2.08% in T2 (7.35 mg kg -1 ), while total nitrogen increased by 50.2% and 101.7% in T1 (0.1750%) and T2 (0.2350%), respectively, compared to the control (0.1165%). CH 4 flux declined by 3.05% in T1 (613.69 ± 12.33 g h -1 d -1 ) and 9.72% in T2 (571.45 ± 7.72 g h -1 d -1 ) on day 60, with further reductions of 9.37% (464.91 ± 10.91 g h -1 d -1 ) and 19.36% (413.67 ± 18.08 g h -1 d -1 ) by day 75, respectively. N 2 O flux dropped by 7.62% in T1 (1.843 ± 0.043 g h -1 d -1 ) and 21.83% in T2 (1.560 ± 0.034 g h -1 d -1 ) compared to control (1.996 ± 0.037 g h -1 d -1 ) on day 75, with no significant effect on CO 2 . These findings highlight the potential of biochar in reducing CH 4 and N 2 O emissions while enhancing sediment quality in inland saline aquaculture.

HapMap findings reveal surprisingly asymmetric distribution of recombinogenic regions. Short recombinogenic regions (hotspots) are interspersed between large relatively non-recombinogenic regions. This raises the interesting possibility of DNA sequence and/or other cis- elements as determinants of recombination. We hypothesized the involvement of non-canonical sequences that can result in local non-B DNA structures and tested this using the G-quadruplex DNA as a model. G-quadruplex or G4 DNA is a unique form of four-stranded non-B DNA structure that engages certain G-rich sequences, presence of such motifs has been noted within telomeres. In support of this hypothesis, genome-wide computational analyses presented here reveal enrichment of potential G4 (PG4) DNA forming sequences within 25618 human hotspots relative to 9290 coldspots (p<0.0001). Furthermore, co-occurrence of PG4 DNA within several short sequence elements that are associated with recombinogenic regions was found to be significantly more than randomly expected. Interestingly, analyses of more than 50 DNA binding factors revealed that co-occurrence of PG4 DNA with target DNA binding sites of transcription factors c-Rel, NF-kappa B (p50 and p65) and Evi-1 was significantly enriched in recombination-prone regions. These observations support involvement of G4 DNA in recombination, predicting a functional model that is consistent with duplex-strand separation induced by formation of G4 motifs in supercoiled DNA and/or when assisted by other cellular factors.

This study aims to evaluate changes in ecosystem service values (ESVs) and carbon stock in the Dimbhe watershed (2002–2022) using the benefit transfer method, considering land use dynamics and economic variability. LULC classification was conducted via supervised classification on the Google Earth Engine, and a dual-approach valuation using both constant and inflation-adjusted coefficients was applied. Carbon stock and sequestration were estimated using the InVEST model. Results showed forest cover remained ~ 60% across the study period, with a 22% increase in dense forest and a 6% decline in open forest. ESVs rose from US$ 55 million in 2002 to US$ 116 million in 2012 (+ 110%) before declining to US$ 72 million in 2022 (− 37%), driven by both ecological changes and economic fluctuations. Only 3.9% and − 2.4% of these changes in ESVs during the first and second decades, respectively, were attributed to LULC alone. The watershed stored an average of 30.6 million metric tons of carbon, acting as a sink from 2002 to 2012 and a source from 2012 to 2022, leading to a net carbon loss valued at US$ 12 million in two decades. The study reveals that relying solely on constant value coefficients obscures economic variability in ESVs, emphasizing the need to incorporate actual economic values for more accurate and meaningful estimations. Recent losses in open forest have reduced carbon sequestration potential, underscoring the importance of targeted land-use planning, restoration of degraded forest areas, and implementation of incentive-based mechanisms such as PES to enhance both ecosystem functions and local livelihoods.

Transmission congestion management became a grievous issue with the increase of competitiveness in the power systems. Competitiveness arises due to restructuring of the utilities along with the penetration of auxiliary services. The present study depicts a multi objective technique for achieving the optimal capacities of distributed generators (DG) such as solar, wind and biomass in order to relieve congestion in the transmission lines. Objectives like transmission congestion, real power loss, voltages and investment costs are considered to improve the technical and economical performances of the network. Multi objective particle swarm optimization algorithm is utilized to achieve the optimal sizes of unity power factor DG units. The insisted methodology is practiced on IEEE-30 and IEEE-118 bus systems to check the practical feasibility. The results of the proposed approach are compared with the genetic algorithm for both single and multi-objective cases. Results revealed that the intimated method can aid independent system operator to remove the burden from lines in the contingency conditions in an optimal manner along with the improvement in voltages and a reduction in real power losses of the network.

Utmost of elucidation about the research accomplished in an article is cleared from the objective function of the planning problem. An objective function is a mathematical expression which describe the existing condition of a system with numerous variables, in which alteration of these variables result in optimized value largest or smallest, depending on problem or desired value. That value may be obtained by minimizing or maximizing the objective function. In this paper, a review has been carried out on objective functions of distribution network expansion planning (DNEP). These objective functions have been classified into five main categories: financial, income related, technical, optimal size & location and social & economic. The selection of objective function clearly shows increasing penetration of distributed generation (DG), distributed energy storing systems (DESS) and fuel cells with renewable technologies. Most of the reviewed articles highlight these objectives in details, however; not all fields have been covered in any single work on DNEP. This review article aims to address this gap so that widespread DNEP can be achieved with flawlessness. Substantial information has been offered of research work done in the field of DNEP through this review article which will mitigate the impending researchers from the difficulties of getting apposite supervision.