Volume 21 Issue 6 2026

Circadian dysfunction plays a critical role in the onset and progression of hepatic and renal cancers, driven by the dysregulation of key circadian clock genes, including ARNTL, PER2, and PER3. In hepatocellular carcinoma, disrupted circadian rhythms affect cell cycle control, DNA repair, and metabolism, facilitating tumor growth and metastasis. ARNTL, a central circadian regulator, is often silenced in HCC due to promoter hypermethylation, impairing pathways related to cell proliferation and differentiation. Tumor-suppressor genes PER2 and PER3 are also downregulated, promoting resistance to therapy and poor prognosis through disrupted apoptosis and altered metabolic regulation. Research suggests restoring circadian function could inhibit HCC progression, with chronotherapy emerging as a potential strategy to enhance treatment outcomes. In renal cancers, especially clear cell renal cell carcinoma, circadian dysfunction involves altered ARNTL expression, which interacts with hypoxia-inducible factors (HIFs) to drive angiogenesis and metabolic changes. ARNTL dysregulation fosters tumor development, while downregulation of PER2 and PER3 contributes to unchecked cell proliferation, genomic instability, and disrupted circadian regulation of pathways like Wnt/β-catenin signaling. These disturbances exacerbate oxidative stress and inflammation, fueling tumor progression. Targeting circadian pathways offers promising opportunities for diagnosis, prognosis, and treatment, with the potential to improve patient outcomes by restoring circadian homeostasis.

Radiators play a vital role in diesel generators and industrial machinery by removing excess heat and maintaining safe operating temperatures. Effective cooling is important for improving system performance, ensuring operational safety, and extending equipment lifespan. This project presents the design and performance analysis of a smart multilayer industrial radiator system integrated with digital monitoring technology.The proposed system uses a multilayer radiator structure to increase the heat transfer surface area and enhance cooling efficiency. It is equipped with digital temperature and pressure sensors that enable real-time monitoring of coolant temperature and pressure during operation. In the system, the coolant continuously circulates between the diesel generator and the radiator, carrying heat away from the engine. The absorbed heat is then released into the atmosphere through conduction and forced convection with the assistance of a cooling fan. The study examines important parameters such as coolant flow rate, airflow, temperature variation, and heat dissipation under different operating conditions. Experimental observations, heat transfer calculations, and graphical analyses were conducted to evaluate the overall cooling performance of the radiator system.The results indicate that improved airflow, efficient coolant circulation, increased surface area, and the multilayer radiator configuration significantly enhance heat dissipation and overall thermal performance. In addition, the integration of digital sensors improves system monitoring and enables more accurate performance analysis.Overall, this project provides practical knowledge of smart radiator systems, thermal management, and advanced cooling methods used in diesel generator and industrial applications.

Many PDEs can be cast into infinite-dimensional Hamiltonian system. İn this paper, the Klein-Gordon-Schro dinger (KGS) is considered as a finite dimensional Hamiltonian system. The KGS equation is discretized by a compact scheme and a finite dimensional Hamiltonian system is obtained. Then, we propose and study several accurate numerical methods for solving one-dimensional KGS equation. Discrete conservation laws of the proposed schemes are analyzed. Numerical examples are given to show the accuracy, stability and the efficiency of the new methods.

The systematic integration of Generative AI (GenAI) in Philippine higher education is critically hindered by the absence of validated tools to assess its effective scholarly embedding. While GenAI promises significant enhancements to research, writing, and learning, current evaluation mechanisms fail to determine whether its application genuinely fosters deeper engagement, creativity, and ethical practice, or inadvertently promotes dependency, integrity breaches, and inequity. This gap obstructs the development of evidence-based policies and robust quality assurance. Addressing this need, this study developed and validated the Generative AI Integration Scale (GAIS), a psychometrically robust instrument specifically designed for the Philippine context. Employing a quantitative research design grounded in the Input-Process-Output (IPO) framework, which integrates TAM, UTAUT, and TPACK theories, and adhering to rigorous scale development protocols, initial content validity assessment (S-CVI/Ave = 0.96) guided item refinement. Exploratory Factor Analysis (EFA) conducted on data from 200 graduate students across three State Universities in Region IX revealed a stable three-factor structure: Societal Equity & Critical Vigilance (18 items, measuring equity advocacy, bias awareness, and risk management), Functional Productivity (7 items, assessing efficiency gains and AIsupported creativity), and Institutional Support for Ethical AI (4 items, evaluating trust in transparent, ethical governance). Demonstrating excellent reliability (Overall α = 0.977; subscales > 0.7), the validated GAIS equips Philippine Higher Education Institutions (HEIs) to effectively measure GenAI integration, enabling data-driven decisions to foster responsible, equitable, and impactful adoption aligned with scholarly excellence.

Phishing attacks represent one of the most prevalent and economically damaging threats in contemporary cybersecurity, exploiting counterfeit websites to harvest sensitive user credentials. This paper introduces a machine learning-based phishing website detection framework constructed upon the PhiUSIIL Phishing URL Dataset, encompassing 235,795 labelled URL samples. The original dataset comprises 56 features derived from URL structure, HTML content, and webpage metadata. To enhance model efficiency and reduce computational overhead, a feature selection methodology grounded in Mutual Information (MI) scoring was applied, contracting the feature space from 56 to 20 URL-extractable features with negligible performance degradation. Four machine learning algorithms were systematically evaluated: Random Forest, Decision Tree, Gradient Boosting, and Logistic Regression. The Random Forest classifier configured with 200 estimators delivered superior performance, attaining an accuracy of 97.38%, an AUC-ROC of 0.9973, and robust generalisation through 5-fold cross-validation yielding a mean accuracy of 97.36% ± 0.04%. A deterministic rule-based override layer was further incorporated to manage unambiguous phishing or legitimate signals with high confidence. The complete system is deployed as an interactive Streamlit web application enabling real-time URL classification. These findings affirm that a compact suite of URL-based features, paired with a robust ensemble classifier, yields an effective and practically deployable phishing detection solution.

The post-pandemic shift to hybrid and remote work has driven organizations to identify new ways of reconciling employees with the workplace. In this paper, the researcher explores the concept of office peacocking as an innovative HR approach and its effect on employee motivation. To assess the influence of office peacocking on employee motivation, a quantitative study was developed, and data were collected from 385 workers employed at sites, on hybrid models, and remotely. The findings reveal that office peacocking should be regarded as a two-dimensional construct comprised of personal workspace expression and identity signaling. Cluster analysis indicated the presence of three types of employees such Enthusiastic Advocates, Skeptical Detractors, and Moderate Pragmatists. On the basis of the findings obtained through structural equation modeling analysis, it can be stated that office peacocking positively affects employee engagement, identity expression, and perceptions of organizational support, thus contributing to motivation. The partial mediation role of employee engagement in this relationship was demonstrated; the explained variance of the connection amounts to 26.8%. As far as office peacocking is concerned, it resulted in moderate gains in employee motivation (+0.170 on a five-point scale) irrespective of the working mode employees use. Also, it was determined that office peacocking provided greater motivation gains in low-culture organizations than in others. Overall, office peacocking can be considered an inexpensive HR practice increasing motivation and identity expression among employees.

Transport and communication are essential components of regional development, facilitating economic growth, administrative efficiency, and social integration. Sambalpur, situated in western Odisha, has historically occupied a strategic position connecting eastern and central India. The present study examines the historical evolution of transport and communication systems in Sambalpur and analyzes their contribution to regional development. Using historical and descriptive research methods, the study relies on archival records, district gazetteers, government reports, and secondary literature. The findings indicate that the development of roads, railways, highways, and communication networks significantly contributed to economic expansion, market integration, industrial growth, and social mobility. The study concludes that transport and communication infrastructure has played a crucial role in shaping the socio-economic transformation of Sambalpur from ancient times to the present.

Colorectal cancer (CRC) ranks among the leading causes of oncological morbidity and mortality globally, with approximately 1.93 million new cases and 940,000 deaths reported annually (GLOBOCAN 2022), underscoring the pressing need for innovative strategies in early diagnosis and efficacious treatment. Magnetic iron oxide nanoparticles (MIONPs) have emerged as versatile nanomaterials distinguished by their unique superparamagnetic behavior, intrinsic biocompatibility, substantial surface area, and multifunctional biomedical capabilities. Conventional chemical and physical nanoparticle synthesis routes frequently employ hazardous reagents and energy-intensive processes, thereby stimulating sustained interest in environmentally sustainable biosynthetic alternatives. Biosynthesis of MIONPs utilizing plant extracts, bacteria, fungi, algae, and related biological resources offers a green, cost-effective, and eco-friendly route that simultaneously improves nanoparticle stability and mitigates toxicological concerns. This comprehensive review consolidates recent advances in the biosynthesis of MIONPs, with emphasis on underlying mechanistic frameworks, critical synthesis parameters, and state-ofthe-art characterization methodologies used to evaluate physicochemical attributes. Special focus is accorded to diagnostic applications of biosynthesized MIONPs in colorectal cancer, encompassing magnetic resonance imaging (MRI) contrast enhancement, molecularly targeted imaging platforms, and high-sensitivity biosensing systems. Therapeutic dimensions— including targeted drug delivery, magnetic hyperthermia, and integrated theranostic strategies—are critically examined with reference to preclinical and emerging clinical evidence. The review further addresses biocompatibility, biodistribution, safety profiles, and regulatory hurdles confronting clinical translation. Despite encouraging preclinical outcomes, large-scale production, reproducibility, regulatory compliance, and long-term in vivo safety remain formidable barriers. Targeted future research directed at refining biosynthetic protocols and advancing tumor-homing specificity is anticipated to accelerate nextgeneration magnetic nanoplatforms for precision colorectal cancer management.

The primary challenge for the students to choose an appropriate career path due to expanding academic options, rapidly evolving job markets, and limited access to personalized guidance. Selecting the appropriate path directly influences future opportunities, professional growth, and overall life satisfaction. Traditional career guidance methods, such as counselling undertaken by professionals to help identify and explore the most suitable careers. The awareness amongst parents and students about the availability and importance of career counselling is low stereotyping and biases regarding career choices, affect career decisions. To overcome these limitations, this study developed an AIpowered career recommendation system. The objective is to provide data-driven and personalized career suggestions using Artificial Intelligence (AI), Machine Learning (ML), and natural language processing technologies. In this work four AI models namely, TFIDF,BERT, SBERT, and Hybrid model combining SBERT with Cross-Encoder are applied on the resume / job offer dataset. The Hybrid model achieved the best F1-Score of 94.3%. TFIDF and hybrid model achieved the highest accuracy of 95.5%

Coronary Artery Disease (CAD) is a leading cause of death globally, and the early mechanism and accurate detection of coronary artery lesions are critical for effective treatment. Invasive Coronary Angiography (ICA) is the gold standard to diagnose CAD, but requires a lengthy manual investigation of the angiographic images which depends on different interpretations between clinicians. This project presents an AI-based Clinical Decision Support System (CDSS) for Coronary Artery Lesion Detection. An explainable and automated diagnostic platform is built using a React-based frontend and Flask-based backend. Various Convolutional Neural Networks (CNNs) models have been either adopted or proposed for lesion detection, including LeNet, AlexNet VGG16 ResNet and DenseNet. ICA images are pre-processed using various OpenCV methods like resizing, normalization and noise reduction to enhance image quality for better model performance. Image patches are used to improve vessel segmentation followed by an aggregation of patch-level and image level predictions in the final risk score. To build links to the interpretation of results, we utilize Grad-CAM heatmap visualization, which emphasizes image areas crucial for lesion detection. Built on a React front-end, this interface enables image visualization, feature analysis, model comparison and risk assessment. A rule-based risk stratification module assigns patients into low, moderate, or high risk and gives clinical recommendations. The proposed system highlights the applicability of deep learning and explainable AI in supplementing cardiologists to enhance accuracy, efficiency, AS diagnostics CAD.

The Khadga River Valley of central-western Odisha represents an important yet insufficiently explored prehistoric landscape within eastern India. The present study examines prehistoric settlement patterns, lithic assemblages, and raw material utilization across twenty-one archaeological sites identified through systematic field investigations conducted in the upper and lower reaches of the Khadga River Valley. The study documents a substantial assemblage of lithic artefacts comprising cores, flakes, blades, bladelets, choppers, and a handaxe. The assemblages reveal technological features associated with flake-blade industries and demonstrate the continued presence of Levallois and discoidal reduction techniques. Chert and quartz emerge as the dominant raw materials, indicating deliberate selection based on flaking quality and local availability. The coexistence of microlithic industries with heavy-duty pebble tools suggests technological continuity and adaptive strategies among prehistoric communities inhabiting Odisha’s highland river valleys. Comparative analysis with assemblages from the Mahanadi, Tel, and Ong river valleys indicates the existence of a distinct regional lithic tradition in western Odisha. The study further highlights the significance of geomorphology, seasonal mobility, and resource procurement in shaping prehistoric occupation patterns. The findings contribute significantly to the prehistoric archaeology of Odisha and provide a foundation for future geoarchaeological and technological investigations in the Khadga River system.

Lip prints are considered unique to each individual and may serve as a valuable tool in personal identification. Cheiloscopy, the study of lip grove patterns, is increasingly being explored in forensic investigation because of its individuality and persistence throughout life. The present study was conducted to evaluate the distribution of lip patterns among the Khasi population of Meghalaya, a tribal community with distinct anthropological characteristics. A total of 100 individuals (50 males and 50 females) participated in the study. Lip impressions were recorded using lipstick and cellophane tape and examined with the aid of magnification. The patterns were categorized according to the Suzuki and Tsuchihashi classification system. The analysis showed that Type IA was the most frequently observed pattern (37%), followed by Type IB (24%) and Type II (16%). A noticeable gender variation was identified, with Type IA occurring more commonly among females (22 cases), whereas males showed nearly equal prevalence of Type IA (15 cases) and Type IB (14 cases). These observations provide baseline cheiloscopy data for the Khasi population and highlight the possible role of lip prints as supportive evidence in forensic identification.

The volatile nature of stock prices, influenced by various internal and external factors, makes forecasting stock prices a complex challenge. Consequently, an approach is needed that can generate accurate and stable predictions to support investment decision-making. Advances in artificial intelligence technology, particularly machine learning and artificial neural networks (ANNs), offer alternatives for modeling complex, non-linear patterns in time-series data. One widely used ANN model is the multilayer perceptron (MLP). The quality of the input features highly influences the performance of the MLP model. This study integrates the Forward Selection (FS) feature selection method to improve the MLP model’s performance in stock price forecasting. The experimental results show that the individual MLP model has captured data patterns quite well. However, the model’s performance improved significantly after integration with the Forward Selection method. The best model achieved an R² of 0.8658, a Mean Absolute Error (MAE) of 0.0331, and a Root Mean Squared Error (RMSE) of 0.0462, outperforming individual neural network models.

Several global questions as transport, energetics, ecology, water service and control on the household waste of regulation of negative consequences dealing with the prognosis degree of urbanization of Tashkent city for the coming decades have been discussed. The problem of chronic traffic jams in Tashkent can be solve through implementation “Mobility-as-a-Service” conception. Adaptation the analogic solutions in Tashkent allows in 2030 to decrease the road congestion level to 20-25% and reduce the average travel time in the peak hours from the current 60 minutes to 45 ones. For increasing stability of power supply in cities it is appropriate to consider the possibility using decentralized solutions such as virtual electric stations and local micro networks. In Tashkent conditions the similar technologies can reduce power outages in the supply of electricity to 30% and provide up to 15% energy savings because of decentralized generation and load optimization. For improving the ecologic situation, the perspective instrument can be implementation of “digital city’s double”. For Tashkent implementation of the analogical platform can be base for more rational urban planning, monitoring of state green areas and controlling the air quality. Using “digital double” allows prognose effectively the heat islands, define optimal places for greening and controlling the pollution level. On the predicting estimations it can increase green spaces area in 2030 to 15% and also reduce impact of the heat islands and PM2.5 concentration to 10-12% because of the exact planning and integration of the green technologies. For solving problems of outdated water supply networks and great water losses to Tashkent is required transferring to smart controlling systems. Adaptation of similar solutions in Tashkent allows to increase efficiency of working the water supply networks, reduce losses to 15-20%, decrease emergence situations number to 15% and provide uniform distribution of water in the new residential areas. For increasing efficiency working with waste, we need to develop the modern complex of reproduction and implement system of the separate collection of waste. For Tashkent implementation of similar practices allows to increase reproduction level of waste from current 6-10% up to 35-40% in 2030, reduce the load on landfills and decrease the methane emissions.

There is a growing disconnect between traditional, lecture-based systems and the practical demands of the modern, VUCA (Volatile, Uncertain, Complex, and Ambiguous) workplace, resulting in significant employability and learning “transfer” gaps. To acknowledge this, the paper theorizes skill-based learning as an experiential pedagogy bridging the gap between academic theory and real-world applications. The study is grounded on the core theories of Dewey, Kolb, Piaget, and Vygotsky, and investigates how active involvement, real-world problem solving, and specific scaffolding in a learner’s Zone of Proximal Development (ZPD) can successfully develop both technical hard skills and crucial soft skills. The evidence demonstrates that a transition to these experiential models significantly enhances the industry-readiness of learners over traditional methods. Moreover, the effective transfer of these competencies to the workplace is highly dependent on learner motivation, authentic performance-based assessments, and the integration of modern technologies such as AI and AR/VR. The paper concludes with a discussion on the key implementation challenges, such as curriculum reform and enhanced industry-academia collaboration, to develop a skilled, future-ready workforce, in line with contemporary policy frameworks like India’s NEP 2020.

This systematic literature review examines the existing body of research at the intersection of skillbased learning, industry collaboration, and corporate readiness in higher education, with a specific focus on management students. A comprehensive search of the Scopus database was conducted using five search strings targeting key constructs: the core skill–industry–readiness relationship, management education context, SEM/mediation methodology, industry collaboration as mediator, and the Indian management education context. The search yielded 13,827 records across all five strings (S1: 744; S2: 457; S3: 8,588; S4: 2,512; S5: 1,526), which were reduced to 12,426 unique records after deduplication. Following a rigorous keyword-based title–abstract screening process, 3,668 papers were identified as relevant, of which 1,834 were classified as highly relevant based on matching three or more core constructs. Bibliometric analysis reveals an exponential growth in publications from 6 papers in 2010 to 887 in 2025, with India, China, Australia, and the United Kingdom emerging as the top contributing countries. The review identifies a critical research gap: while 577 papers address the intersection of industry, skills, and readiness, only 2 papers examine industry collaboration as a mediator in this relationship, and none do so in the specific context of management education using structural equation modelling. The findings provide a robust empirical and conceptual rationale for investigating the mediating role of industry collaboration between skill-based learning and corporate readiness among final-year management students.

The “Universal calculator” program utility in the framework of Matlab medium has been proposed. Unlike from widely used electronic calculators it allows to draw the graph of arbitrary function if its analytic shape or table data are given, to solve any nonlinear equation if we enter its form, to find the solutions of given linear equations system, to calculate the defined integral value, to solve differential equation with known initial condition, to reveal the closest value to the real value when one’s experimental data is given. In particularly, the proposed utility allows to reveal object shotting at an angle to the horizon by initial velocity and coordinates of object are known. Two cases of object’s motion: the air resistance without taken into account and when taken into account by the initial velocity of object and target coordinates are known have been demonstrated. The new representation and transcendent equation allowing to reveal the shooting angle on horizon of an object by known values of the initial velocity of object and target coordinates have been obtained. The proposed utility can be useful in educational process for any natural and technical branch’s teachers of universities and a wide range of researchers. When user is working in the framework of this program utility then one can solve the physical task carrying out it essential fast and independently, observe obtained results graphically, discuss them and conclude an analysis. The developed ourselves methodic approach is directed for developing creative abilities of students through solution of physical tasks. This procedure allows to create on students the vivid imaginations on the physical processes and laws.

As the mission of forward-looking universities is to educate tomorrow’s leaders, it is essential to develop future professionals’ soft skills and communicative skills in English to meet linguistic and cultural challenges of international communication in the modern business world. Hence, soft skills development is a topical question for business and academia worldwide. Classroom simulations have proved one of the best ways for soft skills development, as they allow ESP/EAP teachers and learners to focus both on language and on real-life situations. In this paper, we present our successful experience of introducing Engineers without Borders (EWB) simulation for undergraduate and graduate ESP/EAP students. The goal of this simulation was to develop students’ soft skills by creating a real-life situation of business communication with international partners. The simulation tasks included developing the writing project rationale and overview, writing emails to potential project leaders and funding organizations, advertising the project, and presenting the latter to the general public. The results of this study show that participation in the EWB simulation has helped students to develop their communication skills in English along with such soft skills as the skills of negotiation, project management, collaboration, critical and creative thinking.

This study investigates the financialization phenomenon in Indian commodity futures markets using a market integration approach. Daily data covering eight commodity futures namely Crude Oil, Natural Gas, Gold, Silver, Aluminium, Zinc, Lead, and Nickel alongside three financial market indices (BSE100, INR/USD, CCIL Liquid Bond Index) are examined. Employing the VARMA-DCC-GARCH model, we analyse return and volatility spillovers, dynamic conditional correlations, time-varying hedge ratios, and optimal portfolio weights. The VARMA-DCC-GARCH model reveals substantial volatility spillover from equity, forex, and bond markets to most commodity futures, while the reverse spillover is primarily confined to Crude Oil, Zinc, and Lead. These findings provide evidence of moderate financialization in Indian commodity markets—lower than in developed markets but comparable to China. Gold functions as a safe-haven asset, while base metals serve as diversifiers. Hedge ratios and portfolio weights vary considerably around the 2008–09 Global Financial Crisis and COVID-19, highlighting the importance of dynamic portfolio strategies.

Autonomous navigation of indoor mobile robots still poses a significant research problem, owing to the existence of complex obstacles, uncertain surroundings, and the need for real-time decisions. Traditional path planning methods have been found to be deficient because of their inability to adapt easily, premature convergence, and poor navigation results in crowded indoor environments. To overcome the weaknesses of the conventional path planning techniques, this study attempts to propose an AI Reinforcement Learning – Grey Wolf Optimization (AI-RL-GWO) method for autonomous indoor mobile robot navigation. The AI-RL-GWO framework adopts a multi-objective fitness function incorporating parameters such as the distance of the trajectory, obstacle avoidance, and smoothness of the path to generate collision-free and optimal trajectories. The efficacy of the suggested framework is investigated in different scenarios of indoor environments such as sparse, moderate, and dense obstacle environments and compared with A* Search, Particle Swarm Optimization (PSO), standard Grey Wolf Optimization (GWO), and Improved Grey Wolf Optimization (IGWO). From simulation results, it is observed that the suggested AI-RL-GWO framework provides shorter and smooth navigation path while ensuring better obstacle avoidance. In the dense obstacle environment, the suggested framework obtained an average path length of 143.1 m with 97.7% success rate, which is superior to the compared algorithms. Besides, from convergence analysis, it is seen that the suggested framework provided a lower fitness value and better optimization stability owing to the adaptive reinforcement learningbased searching approach. The outcomes have proved that the AI-RL-GWO framework can balance exploration and exploitation successfully.

One of the first important step in informed data analysis is detection of outliers. Even the case where the final values are considered to be often incorrect. Calculations where they can provide very important information in some times also. So it is very important to detect them before modeling and analysis, they are much smaller or much larger than the majority observations. In this paper we take annual rainfall data in India from 1985 to 2020 for detecting and eliminate outliers using different ARIMA models. The Root Mean Square Error (RMSE) criterion is used for selecting the best model for detect and eliminate the outliers. Different ARIMA models are empirically tested for annual rainfall data in India to detect outliers by using minimum RMSE.

In most low-resource contexts, postpartum depression is poorly identified, and maternal mental health literacy is constituted by the multidimensional social and communication networks. This study explores the effect of information sources and risk communication on predicting knowledge of postpartum depression among Northern Nigerian women. The survey was cross-sectional among women of reproductive age in Niger and Yobe States, and the data were analysed using Partial Least Squares Structural Equation Modelling to determine how well the various communication pathways predicted each other. Based on the Health Belief Model, the structural model indicates that interpersonal networks, medical personnel, religious leaders, radio, and digital platforms have significant and positive predictive influences on postpartum depression knowledge that jointly explain a large percentage of the variance in the awareness of women. The strongest predictive value is communication by medical staff and online platforms, which is followed by family networks and religious leaders, which shows the key role of trust and accessibility in health beliefs formation. On the contrary, the predictive influence of newspapers, magazines, television, indigenous media and opinion leaders in the model is weak and statistically insignificant. This study has shown that perceptions of susceptibility, severity and benefits of postpartum depression are socially mediated and not acquired individually through an explicit modelling of the explanatory power of the various sources of communication. The results give evidence-based recommendations on effective maternal mental health interventions that focus on trusted interpersonal, religious and medical communication avenues to enhance awareness and prompt action to postpartum depression.

Cloud computing is beneficial in a wide range of applications and has been envisioned as the development of information systems design for businesses, training centers, and other corporate sectors. Cloud servers enable customers to store data and use greater on-demand cloud-based services without any of the hassles of managing their programs, infrastructure, and data continuously. It transfers data provided by the public cloud service to cloud storage systems, relieving customers of unnecessary burdens such as physical data ownership control. While the benefits of cloud computing are greater, there are new risks to data security as a result of physical ownership of outsourced material. Users are storing confidential material, and so that they no longer have authority over the operations or their encrypted information, there's a need to develop robust security measures that prevent unwanted access to system functionality and user data. Safety is perhaps the most critical aspect of cloud computing since it ensures that client data is stored safely on the cloud. Cloud computing is a scalable, cost-effective, and time-tested method of delivering corporate services. Cloud computing's primary objective is to provide readily scaled access to computer resources to boost a company's efficiency. The main goal of this research is to examine the issue of cloud data storage, to conduct an efficiency study on the encryption technique, and to provide an understanding of cloud & security vulnerabilities.

Maintaining the required temperature of food, beverages, medicines, and other temperaturesensitive items during transportation is a significant challenge, particularly in the absence of portable temperature control systems. Conventional insulated bags are unable to actively regulate temperature, resulting in spoilage of perishable goods and reduced effectiveness of medical supplies. This paper presents the design and implementation of a solar-powered portable dual-temperature box based on thermoelectric (Peltier) technology. The proposed system employs a Peltier module to provide both heating and cooling by reversing the polarity through a Double Pole Double Throw (DPDT) switch. An Arduino Uno continuously monitors the internal temperature using a thermistor sensor, and the measured temperature is displayed in real time on the serial monitor. The system is powered by a rechargeable lithium-ion battery pack, which can be charged using a 100 W solar panel through a charge controller, enabling portable and sustainable operation. Experimental evaluation demonstrated that the prototype reduced the internal temperature from 28°C to 19.6°C in cooling mode and increased it from 28°C to 47°C in heating mode within 30 minutes, confirming the effectiveness of the proposed design. The developed prototype offers a compact, low-cost, energy-efficient, and environmentally friendly solution for portable temperature-controlled storage with potential applications in food transportation, healthcare, and outdoor activities.