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The basic aim of the research at hand was to investigate the effects of conflict management styles of school principals on teachers' job performance. To meet the major objective of the study, the targeted population was comprised of public sector secondary schools in the Punjab province. As the population was most scattered so the researchers used multi-stage sampling techniques to approach the whole population. At the 1st stage Gujranwala division was randomly selected through simple random sampling technique. At the 2nd stage, 176 secondary schools from three districts viz. Narowal, Gujranwala, and Sialkot were selected randomly. Among these 176 schools, 100 were male and 76 were female secondary schools. At the 3rd stage, 532 SSTs were randomly selected from the respective schools. It was a quantitative study and a survey technique was used to conduct it. The researchers used two questionnaires namely, FOCI for measuring school principals' styles of managing conflicts and TPES for measuring their respective teachers' job performance. Using MS Excel 2010 and SPSS version 24, statistical procedures (t-test, ANOVA, and regression analysis) were applied to analyze and interpret the data. It was determined that there was a strong correlation between teachers' job performance and the conflict management styles used by school leaders. Moreover, in conflict management styles, integrating and obliging styles had a positive effect whereas avoiding, compromising and dominating styles negatively effects teachers' job performance. The researchers have recommended that mix method research may be applied to validate the results of the study. Beyond further research, it is suggested that school Principals should be trained for better conflict management.

The study was designed to investigate the perception of teaching faculty and learners regarding blended learning, i.e. the mixture of classroom and computer-equipped learning. The population of the study was comprised of students and teachers of university of Okara. The researchers used purposive sampling technique to collect the data. The interview protocol was used to collect the data. The interview protocol was drafted and finalized in the light of expert opinion. Total 15 (7 teachers and 8 students) interviews were conducted. The participants were personally contacted by the researchers and finalized the time for interview. The study concludd that the participants have sufficient understanding and know-how about blended learning. The way of teaching was adopted to disseminate educational information and up-to-date knowledge to the students sitting at home. As it was a newly adopted method so it has many procedural pros and cons thus it was the need of us to explore the perception of the teachers as well as students.

Spiders contribute significantly to natural pest control in agriculture. Here, we investigated the biodiversity, population dynamics and predatory activities of spiders in the maize-growing districts (Kasur and Lahore) of Punjab, Pakistan during 2018–2019. Various collecting methods including pitfall and yellow pan traps, manual hand-picking, beat sheet, and sweep nets were used to collect spiders on a biweekly basis. The collected 12,894 individuals represented 45 different species, 27 genera, and 13 families. The major families recorded were Araneidae (32.92%), Lycosidae (20.43%) and Salticidae (11.12%) comprising >64% of the overall abundance. Neoscona theisi (Araneidae) was found to be most dominant species (11.61%). Moreover, the species Macracantha hasselti (Araneidae) and Atypena formosana are reported for the first time from Pakistan. Besides, the estimated species diversity from both districts was 97%. The Margalef's index was highest in Kasur (D = 5.08) and lowest in Lahore (D = 4.95). The values of the Shannon-Wiener and species evenness indices were similar in Kasur and Lahore suggesting similar diversity levels and an even distribution. Spider abundance was highest in April and lowest in June and July. These findings will help to develop effective Integrated Pest Management strategies in maize growing areas across the world.

Abstract Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the spectra-based classification of 11,408 B-type and 2422 hot subdwarf stars. The study employs baseline correction using Asymmetric Least Squares (ALS) to enhance classification accuracy. It applies the Pan-Core concept to identify 500 unique patterns or ranges for both types of stars. These patterns are the foundation for creating Support Vector Machine (SVM) models, including the linear (L-SVM), polynomial (P-SVM), and radial basis (R-SVM) kernels. Parameter tuning for the SVM models is achieved through cross-validation. Evaluation of the SVM models on test data reveals that the linear kernel SVM achieves the highest accuracy (87.0%), surpassing the polynomial kernel SVM (84.1%) and radial kernel SVM (80.1%). The average calibrated accuracy falls within the range of 90–95%. These results demonstrate the potential of using spectrum-based classification to aid astronomers in improving and expanding their understanding of stars, with a specific focus on the identification of hot subdwarf stars. This study presents a valuable investigation for astronomers, as it enables the classification of stars based on their spectra, leveraging machine learning techniques to enhance their knowledge and insights in astronomy.

Bovine colostrum is the initial milk produced after birth that contains high levels of immunoglobulins and antimicrobial peptides. This study examined the production of bovine colostrum powder using two methods including spray- and freeze-drying. Spray-drying involves quickly drying a liquid or slurry with hot gas, while freeze-drying is a low-temperature dehydration process that preserves the product by freezing it and at reduced pressure. The aim was to compare the effectiveness of spray-drying versus freeze-drying in producing colostrum powder, focusing on protein and defensive protein content. The freeze-drying sample contained higher levels of protein and defensive proteins (IgG and IgA) compared to spray-drying. The DPPH value was high in freeze-dried samples, while higher values of FRAP, TPC, and ABTS were found in spray-dried samples. Microbiological analysis indicated freeze drying generally yielding lower counts. Sensory evaluation suggested that spray-dried colostrum scored higher for flavor and aroma, while freeze-dried samples were preferred in appearance. Overall, the study underscored the distinction between spray=drying and freeze-drying in producing bovine colostrum powder, with implications for nutritional content and sensory attributes.

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

Oil refineries have become increasingly more efficient over time. Therefore, huge efforts are being made to invest in better processes and technologies that save energy and maximize the production of high-value products, particularly, gasoline. In this study, two scenarios are proposed to upgrade the Sarir Oil Refinery for increasing its capacity from 10,000 BPD to 120,000 BPD by adding new units of vacuum distillation and Delayed Coking or Fluid Catalytic Cracking (FCC) unit. The production rates of all units are obtained through material balance calculations. Finally, an economical evaluation is carried out to determine if the proposed projects meet the profitability criteria of the refinery and to decide which refinery scenario is techno-economically feasible and maximizes the production of gasoline more than the other. The observational results revealed that the best refinery scenario is the one that uses atmospheric distillation and FCC units as it has less payout time (3.6 years), higher internal rate of return (110%) and higher production of gasoline.

Abstract Mobile edge computing (MEC) reduces the latency for end users to access applications deployed at the edge by offloading tasks to the edge. With the popularity of e-commerce and the expansion of business scale, server load continues to increase, and energy efficiency issues gradually become more prominent. Computation offloading has received widespread attention as a technology that effectively reduces server load. However, how to improve energy efficiency while ensuring computing requirements is an important challenge facing computation offloading. To solve this problem, using non-orthogonal multiple access (NOMA) to increase the efficiency of multi-access wireless transmission, MEC supporting NOMA is investigated in the research. Computing resources will be divided into separate sub-computing that will be handled via e-commerce terminals or transferred to edge sides by reutilizing radio resources, we put forward a Group Switching Matching Algorithm Based on Resource Unit Allocation (GSM-RUA) algorithm that is multi-dimensional. To this end, we first formulate this task allocation problem as a long-term stochastic optimization problem, which we then convert to three short-term deterministic sub-programming problems using Lyapunov optimization, namely, radio resource allocation in a large timescale, computation resource allocating and splitting in a small-time frame. Of the 3 short-term deterministic sub-programming problems, the first sub-programming problem can be remodeled into a 1 to n matching problem, which can be solved using the block-shift-matching-based radio resource allocation method. The latter two sub-programming problems are then transformed into two continuous convex problems by relaxation and then solved easily. We then use simulations to prove that our GSM-RUA algorithm is superior to the state-of-the-art resource management algorithms in terms of energy consumption, efficiency and complexity for e-commerce scenarios.

Abstract In a dynamic financial ecosystem, digitalisation is supporting banks in revamping their business processes to be more efficient, reducing costs, coping with customers’ evolving demands, and keeping them abreast of market competition. This two-stage research aims to investigate three issues in Pakistan’s banking industry from 2006 to 2020: (i) banking efficiency; (ii) the impact of digitalisation on banking efficiency; and (iii) banking efficiency’s absolute and conditional convergence. In our first-stage analysis, bootstrap data envelopment analysis has been applied, which exhibits bias-corrected overall, pure, and scale efficiencies of 74, 77, and 96%, respectively. In the second-stage analysis, we executed Tobit and two-step dynamic panel data system generalised method of moments (DPDSYS-GMM) models, and the results uncover that digitalisation has a positive influence on banking efficiency. Findings confirm that return on assets, bank size, interest rate, and gross domestic product growth rate have a positive association with banking efficiency. Our research reveals that state-controlled banks outperform their private sector and special-purpose counterparts. Our DPDSYS-GMM findings validate β and σ-convergence, implying that initially, low-efficient banks caught up (converged) to the more efficient opponents, reducing cross-sectional efficiency dispersion and attaining common equilibrium. The findings of banking efficiency conditional β-convergence assert that the adoption of digital technology has played a critical role in the convergence process and that digitalisation has acted as a catalyst for less efficient banks to catch up significantly faster to their more efficient rivals. This study displays digitalisation’s disruptive influence on the overall transformation of Pakistan’s banking system in the modern era, resulting in significant efficiency gains.

Abstract This review covers the significance of green banana resistant starch (RS), a substantial polysaccharide. The food industry has taken an interest in green banana flour due to its 30% availability of resistant starch and its approximately 70% starch content on a dry basis, making its use suitable for food formulations where starch serves as the base. A variety of processing techniques, such as heat‐moisture, autoclaving, microwaving, high hydrostatic pressure, extrusion, ultrasound, acid hydrolysis, and enzymatic debranching treatments, have made significant advancements in the preparation of resistant starch. These advancements aim to change the structure, techno‐functionality, and subsequently the physiological functions of the resistant starch. Green bananas make up the highest RS as compared to other foods and cereals. Many food processing industries and cuisines now have a positive awareness due to the functional characteristics of green bananas, such as their pasting, thermal, gelatinization, foaming, and textural characteristics. It is also found useful for controlling the rates of cancer, obesity, and diabetic disorders. Moreover, the use of GBRS as prebiotics and probiotics might be significantly proved good for gut health. This study aimed at the awareness of the composition, extraction and application of the green banana resistant starch in the future food products.

Abstract Background Elucidating the mechanism of fiber transformation underlying microbial metabolism is critical for improving fiber-rich silage digestibility and preserving silage energy for ruminant nutrient absorption. However, few studies have combined quantitative microbial function and transformation products in silage to explain this mechanism. Here, we constructed a workflow to detect the substrates and products of fiber transformation in mixed silage of Sesbania cannabina and sweet sorghum (SS) and combined the absolute quantification 16S rRNA sequencing to reveal this mechanism. Results The synergistic effect of Lactobacillus cocktail and cellulase (LC) simplified the microbial diversity and minimized the microbial quantity, making Lentilactobacillus buchneri the dominant species in SS silage. As a result, the LC-treated silage had greater lactic acid content, lower pH value, and less NH3-N content. The indigestible fibers were significantly decreased due to the synergistic effect of the Lactobacillus cocktail and cellulase. Changes in microbial structure during ensiling also resulted in metabolic alterations. The increased levels of microbial enzymes, including β-glucosidase and sucrose phosphorylase, involved in starch and sucrose metabolism led to the enrichment of monosaccharides (including glucose, xylose, mannose, galactose, ribose, rhamnose, and arabinose) in the LC-treated silage. We found that L. buchneri was positively associated with β-glucosidase and sucrose phosphorylase, reflecting the crucial contribution of L. buchneri to fiber decomposition in SS silage. Conclusion Using an absolute quantitative microbiome, we found that LC treatment decreased the microbial biomass in SS silage, which in turn promoted the energy preservation in the SS silage. The cooperative interaction of the Lactobacillus cocktail and cellulase improved the fiber decomposition and in vitro dry matter digestibility rate by changing the microbiome structure and function in the SS silage, providing guidance and support for future fiber-rich silage production in the saline-alkaline region. Graphical Abstract

Negative thermal expansion (NTE) defies the conventional wisdom of lattice dynamics and offers a novel way to control the expansion coefficient and address some challenges in modern science and technology. From an application standpoint, materials exhibiting NTE across a broad temperature range, including room temperature, are highly sought after. In this work, we propose the multi-doping strategy and prepared a series of rare-earth-based lithium oxides featuring negative thermal expansion, with the objective of adjusting their temperature range. The phase transition temperatures of these materials range from 202 K to 467 K, demonstrating a linear correlation with the average ionic radius of the rare-earth elements. By capitalizing on the synergistic effects of four rare-earth elements, we expand the full width at half maximum of the thermal expansion peak in Li(ErYHoDy)0.25O2 from 1.6 K to 14.5 K, a notable improvement of nearly tenfold is achieved. Additionally, we investigate the low-temperature magnetocaloric effects and inverse barocaloric effects within these rare-earth-based lithium oxides. Temperature- and pressure-dependent Raman scattering measurements indicate that the phase transitions and the barocaloric effects are associated with the stretching mode of the Li–O bond and the vibrational mode of the YO6 octahedron.