The convergence of machine learning (ML) and business intelligence (BI) has transformed the landscape of management information systems (MIS), enabling a shift from static reporting and descriptive analytics to predictive and prescriptive decision support. This study examines the critical factors influencing the adoption and effectiveness of ML-driven BI systems within MIS frameworks, focusing on organizations in Bangladesh. Grounded in the Technology Acceptance Model (TAM), the Technology-Organization-Environment (TOE) framework, strategic alignment theory, and sociotechnical systems theory, the research adopts a quantitative approach using data collected from 312 professionals across various industries. Structural equation modeling (SEM) was employed to analyze hypothesized relationships among eight key variables.The findings reveal that all hypothesized relationships were statistically significant (p < .001). Perceived usefulness was the strongest predictor of adoption (β = 0.63), confirming that users are more likely to adopt ML tools when they believe those tools enhance decision-making and work performance. Strategic alignment between ML initiatives and business goals also had a strong positive effect on system effectiveness (β = 0.57), while leadership support (β = 0.61) and a data-driven culture (β = 0.52) emerged as critical enablers of system usage and impact. Interpretability of ML models (β = 0.54) significantly influenced user trust and system acceptance, and ethical governance (β = 0.48) contributed meaningfully to organizational readiness. On the technical side, infrastructure readiness (β = 0.59) and integration capability (β = 0.62) had the most substantial effects on system performance and decision-making efficiency.These results suggest that the success of ML-BI implementation is determined by a blend of technical robustness, strategic alignment, ethical oversight, and organizational maturity. The study contributes to the literature by validating a comprehensive, multidimensional model for ML-BI integration within MIS in a developing economy context. It also provides practical guidance for organizations seeking to deploy intelligent decision-support systems. Ultimately, this research affirms that achieving value from ML-enhanced BI requires more than algorithms—it requires leadership, infrastructure, trust, and strategic clarity.

A study was undertaken to investigate the effects of non-antibiotic additives—citric acid, synbiotics, and probiotics—administered through drinking water on broiler growth performance, carcass characteristics, and blood biochemical profiles. A total of 400 one-day-old Cobb 500 broiler chicks were randomly divided into four treatment groups: Control; no additives administered (CON); Citric acid @2.5 g/L water (CA); Synbiotic @0.2 g/L water (SB); Probiotic @0.5 g/L water (PB) and with each group having 4 replicates of 25 chicks. Growth performance metrics, such as body weight (BW), body weight gain (BWG), feed intake (FI), and feed conversion ratio (FCR), were recorded weekly. At the end of the trial, the probiotic-fed group had significantly higher BW (p = 0.018), BWG (p = 0.027), and an improved FCR (1.62) compared to the CON (1.74), CA (1.66), and SB (1.70) groups (p = 0.042). Biochemical parameters showed significant differences in total cholesterol (p = 0.013) and low-density lipoprotein (LDL) levels (p = 0.039), with the PB group showing higher levels. These results suggest that citric acid, synbiotics, and probiotic additives provided through drinking water can enhance broiler growth performance, with probiotics offering the most promising benefits.

As semiconductor devices become smaller, FinFETs have replaced traditional planar MOSFETs. Planar devices face issues like weak electrostatic control and high leakage current at small sizes. FinFETs solve these problems with a three-dimensional structure and multigate design. This improves gate control and reduces short-channel effects. This paper explains FinFET design, materials, and fabrication methods. It highlights how fin geometry affects current flow and device performance. Gate-source voltage (VGS) and drain-source voltage (VDS) are important parameters. These control the device operation in the lin-ear, saturation, and pinch-off regions. Performance factors such as on/off current ratio (ION /IOFF), subthreshold swing (SS), and drain-induced barrier lowering (DIBL) show that FinFETs work well for low-power and high-speed uses. Achieving uniform doping below 5 nm remains difficult. Atomic layer deposition (ALD) helps improve doping control. In summary, FinFETs play a key role in modern semiconductor design by improving scalability and efficiency.

Thin-film solar cells (TFSCs) remain a cornerstone of the global transition toward renewable energy, characterized by consistent reductions in manufacturing costs and steady gains in power conversion efficiency. In addition to electricity generation, TFSCs play an important role in advanced solar thermal cooling, heating, and energy storage systems, where their tunable optical absorption, low thermal mass, and flexibility enable integration with photovoltaic–thermal (PV/T) collectors, thermally driven cooling cycles, and hybrid thermal–electrical storage architectures. This paper provides a comprehensive review of prominent TFSC technologies, including copper indium gallium selenide (CIGS), cadmium telluride (CdTe/CdS), amorphous silicon (a-Si), copper zinc tin sulfide (CZTS), organic photovoltaics (OPVs), and metal halide perovskite solar cells (PSCs), with a focus on their material structures, performance specifications, and current efficiency benchmarks. Compared to state-of-the-art reviews, this article distinguishes itself by addressing next-generation innovations, cross-domain solar thermal–photovoltaic applications, and economic analysis. Specifically, the integration of machine learning and simulation-based material dynamics is examined to accelerate material discovery, process optimization, and the characterization of novel TFPV components relevant to coupled thermal–electrical energy systems. Furthermore, the study explores how additive manufacturing is transforming the industry through the development of high-efficiency electrodes, electrohydrodynamic atomization for thin-film deposition, and the fabrication of flexible solar arrays suitable for thermally integrated and building-scale energy systems, including space applications. By integrating advancements in module efficiency, scalable manufacturing approaches, and techno-economic analysis, this paper positions TFSCs as sustainable, resource-abundant technologies essential for next-generation solar thermal cooling, heating, and energy storage infrastructures.